okay you'll hear me see my screen okay
okay good morning yet today after we
learned the basic nature language
processing and some the ones the nature
language processing and this week and we
will learn the webcast mining so the
webcast manning is an area will relate
it to the many and how pay techniques
that's why we learn a basic and RP and
advanced and help you first then we will
have a deep understanding about the
webtest mining so in previous lecture
first we have our short review about the
previous lecture so the previous
lectures we'll learn the nature language
processing once the natural language
processing application so for the basic
natural language processing we learn
several steps sentence leading
tokenization up us tagging the shallow
parsing and that is for the basic steps
and for the semantic analysis and we
learn the word stem is
and equation so we can use the
dictionary based a method or the
supervised the learning method to help
us identify some different meanings of
words in a different sentence and for
advanced application we learn
information extraction and some test
summarization so that is the main
content we learn the last two weeks so
today's lecture and we will learn
testimony
so first we'll understand the
definitions of the test mining and some
basics process about the web past money
and last we will learn the specific area
of the testam I mean that is opinion
mining sometimes we also call it
sentiment analysis so there's many
different levels for the opinion mining
so from the document level sentence
level and attribute levels and as the
sway levels about opinion mining
techniques so first we know what is a
testimony
so actually we learn the data mining in
the lecture 5 so the test mining it's a
similar techniques with the data mining
compared with the data mining but from
the name of the data mining and the test
mining so we can find may be the input
the input data set is different so for
the data mining we always inputs the
structure to the data so after the data
pre-processing the transformation we can
get them for form data so always have
the form data way input for the
difference the data mining techniques
and for the testimony the most cases
would input in the original webcast so
that's from main we can find some main
difference between the data mining and
testimony
that the test mining and we aim to find
the interesting regularities in large
texture datasets where the interesting
means among triple the hidin and the
potentially useful it is similar like
the data mining we want to find some
hide and occasionally used for
potentially interesting information
behind the surface of the tester and we
want to find some semantic and abstract
information from the surface form of the
tester data so accept the surface
meaning of the tester data we want to
find some more deep semantic and
abstract information so that if may aim
of the testimony so the process of the
deriving high quality information from
the test that is the main objective of
the tester mining the model of the
testimony they have the same structure
with the data mining so we got the input
documents always the documents ok the
web documents we collected from the
website and we select some test the
mining techniques and we got some output
we will find some knowledge pattern and
from the original documents and we can
also find some relationship the
connections between the different web
document and we can predict to some
chance so that is the model of the
testimony so the main difference
compared with the data mining and
testimony so the data mining we are
always professed directly and so if we
call some data so we can process this
data directory for the test mining we
always need some liquid stick processing
or nature language
because we've got the huge web document
we don't want to input this huge web
document into our test mining techniques
so the most cases we'll use some our
liqui stick processing or nature
language processing we just extract some
like if we got the web document so we
can use the some keywords extraction or
some taxes summarization or some
information extraction through this kind
of the techniques we just extract some
most important topics or most important
sentence from the original web document
then we'll reuse these keywords where
we'll use this key sentence and input
for our testimony so that's why the the
testimony always need to use some liquid
stick processing or natural language
processing and data mining and it used
to identify the cultural relationship
and in the testimony and we want to
discover the hilt for the unknown
information so and the data mining so it
always includes the structured data and
for the testimony and sometimes we input
is a semi strategy key so like we learn
the information extraction last week
print so we can define some template and
we want to fill this template to convert
some unstructured data to the semi
structured this predefined is a template
data so we can use the disassembly
structured or we can directly input this
all structured data so we can introduce
a direct sentence or directive system
short paragraph into our test mining
techniques so because the data mining we
included structured data so the
structured non-magic transaction data
will residing in relational
warehouse and testimony so the
applications here is a much more diverse
and some Atlantic collections of the
system and format then the wait don't
wait it can support some money on
structure the data test so it can
support the more device a diverse and
eclectic collections so our web document
had sports the different many different
types of media types so that's made
difference between the data mining and
the ties a testimony so that's some
basic introduction about the test mining
so compared with the data mining so we
can find is a main difference you will
have a good understanding of how the
test mining the structure of the data
mining and the testimony similar so they
are both of them the input the data set
so all the input to test a web document
then we will select some mining
techniques so the test mining data
mining so both can supports the
supervised learning and unsupervised the
learning then we can call some output
pattern so we need to evaluate this
pattern and to attract our presented
some model is useful not so the may
process of the test money you can find
that it has a similar steps with the
data mining so the input is a test
original raw test then first we will do
some data pre-processing so the data
pre-processing focused on syntactic or
semantic test analysis so in the test
the pre-processing we will use some
basic natural language processing steps
then we can do some basic syntactic or
analysis and the next step is a facial
generation so future generation we
always focused on the term generation or
some phrase generation so we want to
extract some key terms that's for them
our key phrases so it's an it's kind of
the information extraction so we want to
select we want to generate some features
from the pre-processed as a test and
next step is the feature selection so
from many features and we want to select
the most useful the most important
features to represent our web document
so the feature selection will reuse some
simple accounting or statistic method to
help us to selectusa features and the
next step is a test mining so for this
step we can choose the difference the
supervised learning and also provides
the learning method so it's like our
data mining so it can support the
classification clustering the regression
or something like that prediction so as
many supervise the learning and also
provides the learning method we can also
use the for the test winding steps then
the last step is analyzing the results
so we will evaluate some extracted
patterns and to track these patterns is
it or not if it is not useful then we
can back to the previous step we can
choose a different test mining method or
we can change some parameters and to
repeat again
so that's may process of the testa
mining
[Music]
okay so let's go through one by one for
the age stats with a test the process
first step is
past the pre-processing so this step is
similar like the data pre-processing and
we want to remove some noise or some
error error test and we want to use some
normalization or tokenization techniques
so for example if we got the original
webpage and it always the HTML webpage
so we can remove some useless HTML tags
and we can remove some extra the white
spaces we can convert some percentage
characters to some ASCII characters and
we can remove some special characters
and the lower case all tests and convert
them number words to the new magic form
and remove some useless numbers and
removes stop words so here is some
examples about them can be included in
the test of pre-processing so and the
first one is if we got them are low
terms in the test document so Canada or
something like that so we can use some
lower case and we can change all these
words choose a standard form so like
this Tomcat so if we call some caption
words own or some pcs as some lower come
mixed as a lower case n to the capital
words so we can go change to the lower
case and the second example is about
some are stemming so if we've got this
is the original root word is connect so
if the got some past tense we got some
nouns because I'm because I'm present
and like the connection or something
like that we can all convert the
original word to the stem the words so
they used they have the same root word
Hey so we can use some normalization so
if the rotate is like some ice tomorrow
tomorrow is like that so we can use the
normalization to convert the load test
to choose a normalized word so it's
before and this is on the way if you
have some shot writen so we can also
normalize to choose a standard form so
that ISM that is for the test the
pre-processing okay so and next step is
for is the future generation so for the
future generation we can use the Select
Seco analysis the stop words elimination
index the term selection is kind of the
techniques to generate the features from
the row test and we can view to some
Saurus
okay so this is the main flow about the
future generation so original document
and we can I use the lexical analysis or
we can use some predefined structure and
to convert the original documents choose
them prettifying some a structure the
test and input for the lexical analysis
then the next step we can use some stop
words so for the lexical analysis it is
kind of like we learn the basic steps in
the nature language processing so we can
analyze some prefix we can analyze the
ethics and we can I extract some
connected words like using the half - to
connect to the two words then we can
remove some parked relation marks
so that's Chi
of the lexical analysis we can use the
four days time and for the stop words so
the elimination of the stop words we
want to feel all the useless words for
the retrieval purpose so we want to
remove some useless words so like kind
of the determiner so if we use a POS
tacking so we can find is a different
tag of different words so we can remove
some a determiner so like the earth and
the that is always a useless words then
we can extract some non groups so that
none groups is kind of the like the noun
phrases so we can use a shallow parsing
so to extract some key phrases as always
the noun phrases okay and this noun
phrases we the most important features
always included in this non phrases so
and so next step we can use some
stemming techniques so we can convert
some numb terms and choose root words so
you some stemming then the next step we
can manually index this extracted this
noun phrases then that is indexed terms
so we want to output from the future
generation and this impacts the terms
and we will use the two for the next
step of a trio section so we can select
some important two terms from this index
the terms and two input for our test
mining techniques so the Fate
regeneration can help them so in our
real word so the future generation can
help the reader to quickly find some
important information okay so the input
is original documents but up the Fate
regeneration we want to keep them index
the terms and to represent the original
document
so based on these terms the readers can
quickly find some important information
so we don't need to go through the whole
web document which is the quick rate
this index the terms then we can find
this web document is useful to read or
not so it can support our decision and
the next step is a feature selection so
the feature selection there's three main
approaches we always used the first one
is the filter approach and the wiper
approach and in then approach so the
Advanta proach that is some type hybird
approach so it combines our future and
the wiper approach so let's go through
one by one so the future approach from
the name then we have find the future a
perform a statistical analysis over the
feature space to select some
discriminative subset of the features so
we want to if the input is a set of the
all features and we usually use the
feature approach to just disliked the
best subset and this best the subset and
used for input them learning algorithms
then we can get some performance so the
filter method are generally used as a
pre-processing step the selection of the
features is the independent of the any
machine learning or greasers so it is
independent from this learning
algorithms ok so it can support any
learning algorithms the in stand the
features are selected on the basis of
their scores in various statistical
tests and for their correlation with the
outcome variable so the selected our
strategy is based
scores in various statistical method so
if we use the different statistical the
mathematic model so we can got the
different scores of them different
selection so we can erect the selection
and to select the most important subset
from the original features so that is
for the filter approach and the second
approach is a wide approach so the wiper
approach to survivors a subset of the
features are full first identifying the
band evaluate using the classifier so
compare the main process about the a
filters approach and the wipers approach
we can find so the wipers approach
combined the generator subside and
learning operations okay so we we
generate a subset and this generate
subset will input for the learning of
reasons so the learning algorithms will
also evaluate so this lactate is in
stock that is the best or not so if it
is not best we will return to the
selection steps and to generate some
updates the new subset and to input to
the learning algorithms again okay so
that is main difference or compared with
a filter approach so in wiper method and
we try to use the subset of the features
and a trainer model use them then based
on the increases and withdraw from the
previous model and decide to end or
remove some features from the original
subset so that is the main difference
and the third method is the intended
message so we combine the qualities of
the filter and the wiper method so it is
employment by the algorithms that have
their own built-in feature selection
master
so and from this structure we can say
and it is end of the performance so we
will evaluate the performance of the is
selecting the best the subset process so
the wipers percept oh sorry that is that
is wrong okay so that is for the wipers
process description so in um in band
method so when we generate the subset
and we input for the learning algorithms
and we will also evaluate the
performance and according to the
performance and we will update the
subset again so we will decide to end or
remove features and from your subset
then back to the learning algorithm and
to invalidates the performance again so
that is for this slacked in the past the
subset so compare this three may
approach so we can find the difference
between this filter wiper and event
method so the future method so it is
independent the selecting the subset so
this generated set of the method which
which don't incorporate a specific
machine learning algorithms so the
selection of the specific learning
algorithm and the selecting of the
subsets they are totally independent
steps and for the wipers master so the
in we evaluate on a specific machine
learning algorithm to find the some
optical features so we combine the in
valuating them specifically the machine
learning algorithms and the selecting
the optic optimum the features so we
combine these two steps and for the in
been measured okay so we embed the
features during the model building
process so we always invent the feature
selection during the model building
process so
whether evaluates the performance of
this built model and to update this
subset to hope to keep our built model
always the best model so the feature
selection is done by observing the each
iteration of the model training phrase
there for the filter method so compare
this computation speeding
so the a filter method is much faster
compared to the last two methods the
wipers measured so it is high
computation time for a data set with the
many features and in been measured it's
between the filter method and the wipers
method in terms of the time complexity
it depends up your predefined the
performance so if you defined not high
performance and the time complexity will
be not too complex will not cost it too
much time so it depends you're pretty
fineness of statues find as a
performance and for the future measure
so it's the less prone to overfitting so
and for the wipers method it's high
chance of the over five overfitting
because it involves training of the
machine learning models with the
different comparison of the features
okay so because in the training of the
machine learning models and we compare a
shoe with a feature selection so they
have the hot choice of them overfitting
so the in band method so it compared
they combined the filter and the wipers
message so they try to reduce them over
faking by personalizing the coefficient
of a model being too large so it depends
your predefined the satisfying
performance so we can reduce
some certain over faking cases by the
personalizing of the coefficients of the
model so that is a some a difference
between the filter wiper and in been
Messrs
so these are some specific models we can
involved for the filter wiper and in
been measured so the filter method so we
always used as a correlation the cheaper
test and ANOVA the information game is
kind of the parameters to in to help us
to select the different features and we
used this statistical models and to
calculate some scores of the subsets and
to select some a best a subset so and
for the wipers method and we can use
this all forward flexion the backwards
elimination the stepwise selection to
combine the learning of racers and
feature selection and for the in band
method so we can use the some Allah so
the elastic not and the original
regression that kind of the model and to
help us to fit this model processing
combined with a feature selection so
that is a main difference between this
three main method so this really
mastered our most common approaches we
use the fourth of dangerous election
okay so the next step is a test mining
okay so that is the core step for the
mining process
so and we use the dumb supervised the
learning or some also provides the
learning master so it's same with the
data mining techniques so for the
supervisor Learning tests mining so we
include some training data
and this training data has some pretty
funny labels so then we build the
classification model and then we can
evaluate this model and to track it is
status fine or not and for the
unsupervised learning and we got the
training data set but this training data
set was a label so our still our input
for the training process and the
beauties of unsupervised learning model
the clustering model and to win April
7th test data output put some new data
to evaluate some butyl so that is a 2
machine learning algorithms we used in
the test mining steps so the last step
and analyzing all results we'll check
the whether the build model from the
previous step can turn their useful
knowledge or not so if the detected
pattern is not interesting it is
necessary to fall back to the previous
step so we may need to choose the
different to the test mining techniques
when we change choose different
parameters and to analyze some results
again so that is a that is a last step
for the analyzer pattern results so that
is the five main steps about the test
mining process so it still always our
data mining process it still got the
five steps so a product has the
pre-processing and feature generation
and facial action and test mining and
evaluation so I've made steps ok so then
we will use these five steps and to
those
specific past the mining area that is
opinion funny
okay so opinion mining sometimes we call
it assessment analysis so it's same
meaning so it is important the test
mining areas especially for our web
application so we always want to collect
some users and satisfaction satisfaction
degree or we want to connect them users
review from our developed web
applications so this kind of the opinion
mining we can use to collect them or
reviews from the different users so the
importance of the opinions opinions are
important because whenever we need to
make a decision we want to hear others
opinions so in the past there's no
internet and we can collect some
individuals opinions from our friends or
family face-to-face to it we can collect
some opinions like the questionnaire
okay so for the business and we can send
them surveys and wiki's we can focus on
the group discussed and we can talk with
consultants consultants okay then we can
send some questionnaire and we can ask
them to write the questionnaire and to
collect and analyze the questionnaire so
that's traditional opinion mining method
but currently and the things the
internet is all over the world so it is
kind of the word of months on the web so
we can connect to the opinions from our
web so the user generated with the
meteor so we can use the some Facebook
like the tweeter of so many YouTube so
the many of the
Media types we can use to connect the
opinions from users one can express one
can express opinions on anything in
reviews the forums discussion groups the
blogs so there's no limitations to
express your opinions so it's global
skill so but before we do the opinion
mining so we want to extract some facts
and opinions away we want to identify
this sentence is a fact or opinion so
for example the data from the product to
review about iPhone a few days ago it
was such a nice form the touch screen
was really cool the voice quality was
clear too although the battery life was
not long that is OK for me however my
mother was mad with me and I didn't tell
her because I bought the phone she also
thought the phone was too expensive and
wanting me to return to the shop so this
kind of them example data is from the
product review so the first we want to
analyze some opinions before we analyze
opinion we need to identify the which
sentence includes opinion so we want to
identify which sentence is fact it's
just it's effective description and some
sentence ISM have some opinions
description so then we can identify the
targets of the painting so the targets
includes the object and there are
tributes and then we can also identify
an opinion holder so first step like the
may process of the testimony so the
first step we can use the some syntactic
syntactic or semantic test the analysis
so to identify the fact
and opinion so there are two may types
in the texture information fact and
opinion so the fact it means the
objectives expression about their
entities events and their attributes
like I bought a phone yesterday
okay the most current is attached the
processing techniques for extracting the
effector information and for the opinion
then they will cause some subjective
expression of the segment's attributes
emotions of feeling towards entities
events and their tributes for them I
really love is iPhone so we can find
some statements and emotions from this
kind of the sentence so that's a
difference between the fact and opinion
and and the next step we can the future
generations okay so after we got the
sentence included the opinion okay if we
got some sentence include opinion and
we've just removed these effects in
sentence because we want to mind the
opinions so we just remove this effect
sentences keep just the opinion
sentences so the next step we can use
the feature generation so the future
generation there's may some male factors
we can generate it from the original
opinion sentence so we can extract some
past a target object attributes the
opinion holder and opinion orientation
so the target object so that is means an
entity that can be a product person
invent organization or topic so like
like for example it was such a nice
phone so that is the opinion sentence
right
so from Nate's original sentence we can
find the target product we talked about
foam okay so this entity this target
object is talked about for iPhone okay
so and we go
the touchscreen was really cool and this
is also a painting sentence and this
target object we talked about the
touchscreen okay and then the next term
fire factor is a tribute
so that is object to usually have two
types of attribute so components so like
the phone have done some components or
tributes the touchscreen the battery and
they all also got them properties so
like the size weight colored boys
quality so the foam size wage colored
boys quality this belongs to the
properties and the touchscreen batteries
these this kind of the attributes belong
to the components so and we can also
divide the attributes into the explicit
and implicit attributes so the explicit
attributes so that means it appears in
the painting so like the battery life of
this form was not long so this explicit
attributes the battery life of this
phone so the battery life that is some
the explicit attribute appears in the
opinion okay and some implicit or
tributes not appearing in the opinions
also like this phone is too expensive so
the properties the implicit or tributes
the price of this form so we talked
about the price of this phone but the
price this property price doesn't appear
in that is a painting sentence so
it can be included both explicit and
implicit attributes okay and another
factor is a painting holder so the
person or organization that expresses
opinion so like my mom was mad with me
so the painting holders my mom my my
mother because my mother expressed the
day's an active opinions mad with me so
the mother that is a painting holder
opinion or occasion so it can be the
positive negative and is the neutral so
if I like this one phone the battery of
the life the battery life of this phone
was not so that of a pinion orientation
is native okay so like this the phone is
too expensive it's also negative my mom
was mad with me that's naughty the
positive like the the touchscreen was
this form is really good this form is a
nice form so this kind of that positive
orientation so that's for the future
generation so we can generate some main
factors of the opinion mining so the
opinion mining always can be expressed
by these five male factors okay so the
paintings can be represented by this
structure so a person or organizations
that expresses a positive or negative
segment a particularly tribute of a
target object at a certain time so
there's the five main factors we can
always extract it we can generate it
from the opinion mining sentence so it
includes the target object attribute
irritation opinion holder and sometimes
also include time or location so
objective so the up
active of the opinion mining we want to
give pennant eight document so want to
discover all the Queen two books so we
want to mine the five corresponding
pieces of the information in each
creature books and we that's why we want
to convert some unstructured test to
some structured test okay so it can be
applied to the classification or
clustering techniques so for this
generation for this step we can also use
some for some classification or
clustering techniques and to help us to
extract these five main factors so
opinion types so opinion types can be
divided into the directed opinion and
the comparison opinion so the directed
opinion that means the settlement
expression and one or more attributes of
a target object so the course of this
form is fantastic my mother was mad with
me so this statement expressions just on
one or more attributes of them just one
target object okay and the comparison
opinions that's relations expressing the
similarities so it will include the more
than one target objects so I for eight
is not as good as iPhone 7 so it has a
to target objects eight and iPhone eight
and Ivan seven so we talked about the
relations similarities the comparisons
between these two target objects so
that's some opinion two different
opinion types direct and comparison and
sometimes we can also divide some
opinions types into some explicit
opinions and implicit opinion okay so
the explicit opinion that amines
opinions are attribute explicitly
described in a subjective sentence so
the voice quality of this form is
amazing the camera is too heavy so this
kind of assessment world
we'll be expressed in a subjective
sentence explicitly so and for the
implicit opinion so opinions are
attribute implied in an objective
sentence so for example the hands that
broke in the second day so from this
sentence we can find some a Haida name
so it's just a bonus hands that but it's
just a broken in a second day so we body
is today and it broke in the second day
so it means the quality of these hands
that is too bad okay and please bring
back the old version so it means we're
not we're not that is find with the old
version we want to upgrade to the new
version
okay so this kind of the opinions we
call it implicit opinion okay so we
don't use this any explicit on statement
words so this judgment is high from many
of this sentence so that's another to a
detsen that is from this syndrome we can
divide to the opinions into the explicit
opinion and implicit opinion so that's
two different dividing masters okay so
that's for the second step the fate
regeneration so we want to generate the
five main factors from the opinion
sentence from the second sentence and a
third step that's mmm feature selection
okay so the feature selection we want to
calculate the opinions or rotational so
the feature section is based on the
statistical the analyzes of the
different score of the subset so after
the state regeneration
want to calculate this opinions
orientation score then based on this
orientation score then we can select
some different attachment words with the
different scores
so the pinions and the polarity words
and we dominating the indicators of the
segments and especially some adjectives
adverbs and verbs
okay so that is the three may kind of
the POS packing we want to may include
some statements words so for example I
absolutely love this camera it's amazing
so love that is a verb right so it's
amazing that is a objective so this kind
of the tags always includes the
statement meanings so we can use some
predefined opinion lexical so there's
many kind of the pinyons lexical so we
can use that this pre defines the
opinions Mexico to assign this words is
the segment words or not okay then we
can assign the opinions orientation
score so for example if the positive
opinion words with so we can +1 and it
has the negative opinions words that we
cannot the miners won so and we will
assign the opinions or rotations goes to
all the words then we can final we can
calculate the final orientation score
that is the sum of the orientation
scores of the all opinions word fault
okay so opinions lexical so from this
step we can find the predefined opinion
lexical that is most important we use
the disappearance Mexico to identify
this word is the positive opinion word
or negative opinion word most important
indicators of the settlement opinion so
for example the opinions Mexico used to
identify the pleura two words assessment
lexical and opinion words okay commonly
expressed a positive or negative
statement so like one for beautiful
grapes that is positive negative
horrible mad poor okay so there many
words can be included in the opinions
Mexico so how to generate the opinions
lexical so first a method we can
manually attacked so we can find a
dictionary and manually tag disease
words and build a lexical built opinions
Mexico but this is label expensive and
time-consuming so it is not recommended
so most of the time we use the
dictionary based so we use the state
list and grow the list so example that
dictionary based opinions Mexico step
one and we can build a small set of the
settlement words and we call it seat its
words and with the know positive or
negative orientations so this kind of
the state statement words and we need to
manually tact so we need to tag it is
positive or negative orientation so like
that is a state word sorry so that is an
active orientation so a man you attacked
then in step two and we grow this step
by searching in the dictionary so we can
use a word nut or another online
dictionary for their synonyms and
antonyms okay so we can find some
synonyms sorry
regretful that horrible so that is some
synonyms of the story okay so both of
these an active orientation so then the
next step we can integrate the step one
and the two until no more
new words can be fine so the next step
we can find the synonyms of the
regretful
you can find the synonyms of the bat
then we can grow our state words to a
group okay and after the process
completes a manual inspection step
should be done to clean up the list okay
so after there's no more new words can
be ended to this group then we can
manually inspection so we can track if
there's some error or mistakes in this
surfing processing so we can clean up
some arrow and to finalize this list
okay so that is some may process of the
dictionary based opinions mining we
start with some seed words and to
searching the synonyms from the online
dictionary and integrate the steps and
to find the moral words and the last
step is a manual inspection okay so this
is some available opinions lexy coast
which he used to generate our opinions
lexical
so and in the following tutorials auras
can sew and will be used as a one
catchment lexical that is the we use
this one segment lexical and to Butte or
PHP web application so we will use them
we will write the PHP coding and to use
the some predefined assessment lexical
to analyze some sentence into the
sentence then we can calculate this
sentence final opinions or occasion is
positive or negative okay so but the
current opinions lexical you have the
many issues okay so for example a
positive or negative statement word may
have opposite in different application
domains so they may have the opposite
meaning
in the different target sentence so for
example the camera sucks so that is
means nak T okay and vacuum cleaner
really sucks that's positive so we use
the same settlement words but in
different application domains they may
have the different orientations and that
is some one problems and second problems
a sentence concerning the settlement
words may not express any settlement so
can you tell me which Sony camera is
good okay if I can find a good camera in
the shop I will buy it they have this
two sentence have some settlement words
good but they don't want to express any
statement meanings so they just it's
like just ask question and then this
good and second sentence is good it's
just a condition so increase is a good I
provide it so they don't it want to
express the any statement that is a
second problems okay so if we use the
dictionary based opinion Mexico and the
final output is a positive orientation
but it is not positive orientation and
the last problems in any sentence
without the statement words can also
imply the penis so like this washer used
a lot of water so from this sentence we
can find it wants you express an active
orientation because there is for sure
waste water okay so this sentence
doesn't include any sentiment words but
it can also imply the negative opinions
so this three May issue in a current
opinions lexical if we use some
dictionary based opinions Mexico messer
it's still difficult to solve these
problems so it's
and if code to loiter these problems so
that's why we need to use them more
intelligent method we use we want to
combine some other AI techniques and to
solve these problems
okay so we need to use some combine some
nature language processing techniques so
for example this camera sucks is the
vacuum cleaner are really such so if we
can analyze some relationship between
the premiere sentence and the formal
sentence and we can analyze the
relationship between the formal sentence
and the later sentence and when we find
some Hyden meanings behind this sentence
then it can support us to got the
correct orientations so this kind of
natural language processing techniques
when they combined with test mining to
help us to get the final species it so
that's some issues in the current
appearance lexical okay so then the next
step is for the feature selection so we
can select the statement words we want
to use the for the opinions mining okay
so then the next step opinions mining so
can have the different method to mind
the different opinion pattern so we can
use the document level opinions money
and sentence level and attribute level
okay that is from the road to the
details so we can just analyze the
document level and sentence level and
attribute level so it is go deeper and
deeper so for the document level opinion
money so we want to identify if the
document expresses opinions and what
the pennies a positive negative or the
neutral based on the overall judgment
expressed by a penny holder so we can if
we for our lecture and for the document
level opinions mining we just assume the
each document just expresses opinions on
a single object by a single opinion or
holder so we just assume this is just a
simple opinions mining task so it has
only a single object and the single
opinions holder so we can classify a
document into the positive or negative
statement and use that exist in that
supervise the learning method such as
the Bears decision tree in your network
or some also provides the learning
method the k-means higher-ranking
co-clustering so we can use the features
such as the terms and their frequency
the part of speech the sentiment words
and phrases the rules of the painting
the statement of shifters so this kind
of features we can use the tube built
our classification model and that's for
the document level so we can for example
we can use the some keyword extraction
or test summarization
so we can convert the document to adjust
the several key words or just impose
sentence okay so and we can calculate
this we can use the some pictionary
based or paintings lexico or we can use
the same classification mastered and to
get the a positive or negative
orientation of this keyword and the case
sentences then we can get output results
so that's for the document
level and for the sentence level so we
want to identify if a sentence is
opinion Tate and whether the paintings
is positive and negative on yours so the
subjectivity classification are based on
the supervised the learning model so
this opinions mining and we can use the
it's same as the document Aleppo
opinions mining so we also use a
supervised the learning message to build
to the classification model so the many
complex the sentence have the different
assessments on different objects okay so
for example the Apple is doing very well
in this noisy economy so um this
sentence and they may have the different
the target object so if we talk about
the app okay then the Apple is doing
very well okay that means this is Apple
we want to express the positive
orientation and then the last part of
the sentence the law see our economy so
if we change the target objectives
economy so in current economy ISM is bad
it's lousy so that is kind of the
negative irritation if we change the
target object so the many contacts the
sentence may have the different
attachment on different objects so we
need to extract the different we need to
identify the different target objects
and to extract the corresponding
settlement words for this different
objects and to get the output in
different attachment words and calculate
the different attachment word score and
to get the finalized orientations so
that's for the sentence level opinions
mining and sometimes we can combine the
rule-based opinions money so we can use
a dictionary based opinion Ezard but
sometimes we can also combine the other
methods so such as a rule-based opinions
mining so the simple the
penis orientation score calculation is
not enough sometimes so there is no
reason to hate about this restaurant so
if we use the dictionary based opinions
money and the final exam that is an
active orientation because we all find
is an active statement words but the
correct statement rotation of this
sentence should be positive okay so some
rules should be also ended to support us
to make a decision so simple juice can
be manually created so such as if way
just to find the one net words and the
final orientation is not teeth and it's
just a single positive then that's
positive and if it is an action active
that that should be positive and if the
Nexen positive that should be negative
and if our way we find some desired
value range and that's positive and if
below or above our is designed a value H
that is an active so this kind of the
simple juice can be manually created
combined with our dictionary based in
the opinions Mexico method and yeah and
for the sentence level opinions money
and also as well as a document level
opinions mining the supervise the
learning opinion mining mastered is the
most common method we use the currently
so we can input some training data so we
can the training data can be the
documents or sentence so like would you
manually label the say sentence of this
sentence that's positive this sentence
is not chief then we will select some
differences of classification techniques
and to build the classification model
okay so the next time if we input some
new document or sentence then we will
kokorin to our classification model to
calculate the similarities between the
past
document attach the sentence and our
existing sentence that will get the
final classification results so that is
for the supervised the learning of
pinnies mining and a trivial two level
so the last level is attribute level
apini money so a treaty of the level
opinions mining is also used to discover
the statements on target objects and
there are tributes so for example the
iphone's quality is good but its battery
life is short so this complex sentence
we talk about the target object iPhone
but it includes the two different
attributes the one is co quality another
one is the battery life so the co
quality and the corresponding statement
words is good so that's positive
orientation and for the battery life the
assessment word is shot that's not cheap
operation so that's for the or treaty o2
levels so we can find some detailed
assessment analysis for the different
attributes of the one top one or two
target objects okay the poor performance
in extracting both effective extraction
and abstract is document classification
so the existing algorithms are still
unable to deal with their complex
sentence so actually the the segment
classification a model the accuracy of
the assessment classification model is
based on the attribute extraction okay
so if we can clearly identify these
attributes and
corresponding statement words then it
will have the high precision of our
statement classification model but
currently it's still I'm able to deal
with some more complex the sentence so
for the attribute level opinions money
and we record the three tasks so the
task one we will extract the object or
tributes that have been accommodated on
each document so first we want to
extract them attributes of the target
object and pass the two we were
determining whether the paintings on the
features a positive and negative or the
moocher so we want to match each
statement words choose each or tribute
then determine whether these opinions on
this attribute a positive negative or
neutral then we can group the features
synonyms the past three we can group the
future seems okay
so if there's two attribute to God the
similar orientation so we can group
these attributes and the gods are
synonyms synonyms positive and and that
kind of tasks will be included in a
three okay so that's a three sub tasks
included in the attribute level opinions
money okay so this is an example of a of
the attribute level opinions money okay
so there is a paragraph those my source
on the iPhone 5 Apple available place
like that so it has the one we want to
extract this attribute off this target
object iPhone 5 okay so the retina
displays blah blah blah blah
and something like that so it's a
tribute one we talk about such display a
Retina display and a tribute tool and we
tar is a camera okay so for the retina
display and we can find some description
sentence so the retina display is
awesome as much color and clarity out
there so this kind of the sentence the
corresponding description for the
attributes one display and based on a
dictionary based opinions lexical
measured so we can extract as a segment
words awesome much color clarity or
something like that awesome
and clarity then we can calculate some
orientation score and they may got the
positive orientations and forth
attributed to camera and we also find
some corresponding expression from the
paragraph so the camera as well captures
great images so we we can also analyze
the face as such mental sentence and to
find the final segment of paintings
orientation okay so that also positive
so this kind of the attribute level
opinions money
so first the past we find is attribute
then find is the corresponding
description words and extract the
segment words and to use the some kind
of the dictionary based the pain is mine
in Mexico okay sometimes combined the
rule-based and to get the final segment
orientation so that is for the attribute
level or pain is mining ok so then next
last step so if we got the final
assessment orientation from the original
web documents
the weekend lost the staff we will
analyze the results to detect this
opinion spam so sometimes maybe the
final results it's just a span it's not
a real expression okay so we may got
some fake or unchoose for reviews so we
want to detect this fake or I'll choose
foreign views so we will write them I'm
deserving positive reviews for some
target objects in order to promote them
so write them unfair or Malaysia
negative reviews for some target objects
to damage their reputations of this kind
of the fake all unchoose reviews and
always happens in the online shopping
case so if we go through the eBay or
some Alibaba
so there's many flake and unchoose to
release they just want to damage some
reputation sold they want to just
promote their products so and we want to
identify this kind of them release so
this last step is kind is kind of the
opinion spam detection so the pain is
band detection aims to identify the
every fake reveal all fake reviewer and
figure reveal group and sometimes they
are group okay and this concepts are
clearly related as fake reviews are
lightened by the fake reviewers and
sometimes of the reviewer groups can
form them perform some disaster then
they wait they will provide some wrong
information they will mislead the users
to buy something okay so the detection
of the one type can help the detection
of the others so if we can identify the
fake reviewers of fake review or group
okay and it is the easy to help us to
remove all the fake reviews from these
reviewers
okay so the detection of the one type
can have the detection of the other
types so and we can use some AI
techniques to help us to detect the
opinion spam so the spam detection can
be formulated as the classification
problems with the two class sake or non
fake spend on no spam okay so supervised
the learning is naturally applicable so
example there's some examples so this
kind of researchers have been published
some supervised the learning method and
successfully used before the opinions
spam detection however it is very
difficult to recognize the fake review
so when we go through the reviews so it
is hard to find this is a fake review or
it's a non fake review so because the
spammers can carefully craft a fake
review that is just like the ADI
innocent to review we can't identify
this is a fake or not so the most of
time the most cases we use it to wait
identifying the fake reviewer or fake
reviewer groups first then we will
remove these reviews from all these
reviewers so that is the easy thing to
either wait to help us to identify to
remove the fake reviews okay so and
sometimes we can also use this on
supervised learning method so it based
on the difference are reveal patterns
that is just spending spamming so we
can't identify this instead or not but
we can stressed this reviews have the
most minorities with the other spare
reviewers okay so if we can't clearly
identify this
viewers is their knowledge we can
calculates the similarities and we just
distressed this is much like a span okay
so each model will assigns the new magic
spanning a spanning behaviors for to a
reviewer so we can calculate some
similarity score so all the scores are
combined to produce the final span score
and this method aims to find the
spanners of fake reviewers rather than
the fake reviews so if we can calculate
all the review reduce of these spammers
or the fake reviewers so we can easy to
use some simple san cristobal method and
to find all these reviewers always
published some fake reviews and this
fake reviews so these reviewers it's
much like a spammer so we may remove
these reviewers so we may reduce trick
these reviewers to publish the reviews
in the future so this kind of the soup
also provides to stand detectors we've
always used to find the spammers or fake
reviewers okay so some rate so so that
is the that is the opinions mining we
use this typical tester mining areas of
Kimmie's mining to detailed explain the
five steps of the test mining so
pre-processing the state regeneration
the selection and the test mining we
used as three different levels that
document level the sentence level and
attribute level so for the document and
sentence level so we can use this and
supervise the learning test the mining
techniques and to help us to
get the final segment orientation and
for the tribute level so we first weight
extractors attributes of the target
object then we get a corresponding
attachment sentence and houston said we
can use the dictionary based opinions
lexical to get the final rotation so and
the last step and wait for the analyzing
with their results if so so if we got
the final or equation and we can use
some opinions and the action to identify
this final orientation is fake or not
okay so we briefly explained the
opinions mining problems using the basic
test mining steps so for the director
opinions so if for the direct opinion
that means this opinion sentence or this
paintings document includes some
explicit explicit the statement words so
we always focus on the feature level
analysis okay so we can easy to generate
the main factors of the opinions mining
then we can easily to select some statue
into words and to choose the different
testimony method and for the comparative
opinions things we got the more than one
target object
so the different types of the comparison
so we may need to analyze the different
target object respectively then we need
to use them relationship extraction and
to find some relationship between these
two target objects and for the opinion
span detection so we want to identify
some fake reviews fake reviewers as well
as the reviewer books
so it can support a lot of applications
but the technical challenges are still
huge so as we talk the parent of the
pinyons mexico's do have the many
problems okay but hmm with the
development of the AI techniques I think
it still has the many many research
topics we can do so I'm quite optimistic
we can solve these problems using the
many advanced that they are techniques
okay because we are AI people okay so
that's more there's some references so
more details can be find in this this is
a it's a book okay
so assessment analysis and subjectively
does captain in a handbook of the nature
language processing so talk about the
web data mining so that is the early
version then you can also download some
apply document and that is also another
references you can find the multi tails
so okay so that is the main content
about the today's lecture hey do you
have any questions so yeah if you don't
have any questions we can have a nice
break then we can just have a quick
through about next week tutorials of any
questions
[Music]
okay so
yeah let's have a nice break and we back
- I have a quick introduction X trick
tutorial
you
okay so let's continue okay so the next
wave tutorial we will practice test
money we still use the rapid miner so
one is about the and predict prediction
so that's we talk about the opinion
spend detection so we want to identify
this reveal is there not and second one
is assessment analysis of moving reviews
so if we call some moving views and we
want to identify it is the positive
negative are the nutrients so this kind
of test mining techniques this is very
useful for our web application
development so spam prediction so this
the original data set will have two
attributes and first one that is
original reviews and the second
attribute and we indicates whether this
message is spam or not so we got this
label this training data set then we
want to build a classification model and
to identify this in spare not so this is
several data professions
we need to do so I like the
normalization so we were nominal to the
phenomenal operator so that is several
the data preparations we have practiced
in the previous tutorial so then we want
to use the word thwack
techniques so this kind of the messages
and we want to convert to the WAC and we
can calculate the similarities between
these words so if we call some new sense
we've got some new reviews we can
calculate the similarity and based on
our classification model we can get the
final output label okay so and that is
the whole process about the model
training and evaluation so we unload our
data then we split the data and between
our data and a generative model that
apply this model and to get some measure
performance so if we got some new test
data we can apply this model and to get
a measure performance okay so that is a
and 11 Lee with Slade the original data
into the two samples the 70% we use the
for the training data sets and insert
events we use the for the pass the data
set is for the validation sample okay
okay so that's for the spam detection
and for the a moving new segment
analysis and we've got the data original
data that includes them reviews and this
reviews may include some segment words
okay so this is some reviews so the
opening sequence of this gem is the
classical and the cattle of
globalization is looked like wonderful
story or something like that so this
kind of the reviews and we want to use
some segmental analysis we want to use
some test mining techniques and this is
we use the classroom assert we used also
provides the learning method to
calculate some scores of the different
assessments sentence and this is the
input training it
and after some are clustering and we
bought some this is the confidence of
the positive orientation and they see
sir confidence of the negative
orientation so we can got the scores of
them positive and the son negative then
based on this our calculated this
results and we can count the final
rotation okay so for example so work
moving faster us inside that so this is
positive more than the negative
orientation so maybe the final results
of this reviews is positive ok so that
is some moving release judgment on
Lance's so for your assignment and this
segment analysis this is very useful AI
techniques you may integrate it in your
web application so when you collect some
reviews from the user so like we use the
web speech API web speech API at our
user interface so if you improve some
sentence you ask the users satisfaction
difference then based on the user's
revealed and we can integrate it with
the sentiment analysis and to collect
some segment occupation from the users
so that's kind of AI techniques we can
integrate it for our conversational
checkbox so the lecture okay so the
lecture we will learn from next wave is
some pass the web research so next we
could learn indexing and ranking so
that's a talk about the web search
energy so and the way this also related
to the information retrieval okay web
search engine and information retrieval
they have much similarities between the
indexing and ranking that's the most
important topic we will learn in the
next lecture and
the next week to tourists and will be
still you select a miner to practice
some nature language processing and the
testimony and techniques okay and so the
last week we use the Raptor minor and
the wait pen tutorial and we'll go back
to some web application we'll go back to
them a web api so we've introduced is a
one popular in our p api and we will
also to teach you how to use the
statement analysis in a PHP coding then
you can easily to combine NLP api and
document analysis in your assignment
three okay so in a week the Chris is
saying we will take a quiz about what we
learn in the next week web text mining
so especially for a pain in mind okay so
that's uh some short review about the
next week tutorials and lectures okay so
that's the whole content about today's
lecture
do you have any questions so how about
house going about your assignment to so
some student asked me so can we submit
to the audio recording about the
plantation
so actually in our requirements I said
it should be the screen recalling so if
it's not screen recordings just audio
recording and when we mark your
presentation we we can't match your
presentation slides with your audio that
isn't difficult to match so that's why
we need to use a screen recording so the
Sun screen recording software you
can use the for your assignment tool so
if you use this a match you know if you
use this iOS system you can use a quick
time quick times done so that is a
software you can use the to the
screaming recording so QuickTime players
or a QuickTime Player so that is a one
software it can use for the screen
recording easily and if you use the
window sister I I just know though if
you use the Windows 10 system and they
had some they have some interpreted the
screen recording software so if you
press the window and J I think me window
and change them in chocolate yeah if you
press the is window and the J and there
is the integrated zone primal recording
software so you can click it easily to
start recording and to save it in your
local path then you can upload it in a
peer-review system
yes I'm not sure it's just for the
Windows 10 system adding if you use the
other windows system and you may need to
download some frames with more recording
software but anyway it's easy to use a
screen recording okay so that's for
screen recording questions so you have
any questions about your assignment - or
any questions about the final exam
[Music]
that's for the segmental analysis I
think in the rapid minor and if you
accustomed settlement to reviews and we
convert these reviews and this word to
the wack vectors because we want to use
the clustering method and then we can
use it if we will convert these words to
the rector's we can calculate the
similarities between these words then we
can use the clustering message to
calculate to make the similar words into
a same cluster so that is for the word
back in the segmental analysis use the
for the also provides the learning
method
[Music]
yeah the one non phrase is the Fox
another one is the chinking that's two
noun phrase so as we learn in the
lecture that each sentence always can
convert to a noun phrase plus the verb
phrase right so the first noun phrase is
the Fox and the eight the chinking that
is the verb phrase and this verb phrase
can also divide it into a verb plus noun
phrase 8 is not verb it's a past tense
and is a chicken that is another non
phrase so that's why they have two noun
phrase phrase that means the Fox is a
noun but it's not a phrase the Fox
though is work that is the determiner
right box that is one noun phrase
chicken is another tres emerged some we
merge some several the POS tacking into
the yeah okay in the same phrase
[Music]
any questions yes
when merges a determiner and announce to
a one none phrase yes correct
[Music]
any questions you can make a small
change yeah you can make a small change
the course I think for your assignment
to you may have the more clear
understanding because we have learnt
already AI techniques we have learned
old web api's so you may have the more
clear understanding so you can add some
more description you can also to make
some small changes to make it more
clearly to support your assignments
through a development but you can't make
a huge change if you want to make a huge
change yeah yeah then you should update
your assignment one proposal
[Music]
okay any questions how about other
students yeah thank you for your three
[Music]
yeah I know you're always the first one
to attend our lecture
[Music]
okay okay so yeah hope you have a good
weekend but if you have any questions
about your starting or assignment and
you can feel free to ask me
yeah
[Music]
hi