Sentiment Analysis in Twitter with Lightweight Discourse Analysis

Sentiment Analysis in Twitter with
Lightweight Discourse Analysis
-Subhabrata Mukherjee & Pushpak Bhattacharyya
Presented By: Naveen Kumar

Outline
 Introduction to Sentiment Analysis
 Motivation
 Introduction : Approach
 Discourse Relations
◦ Discourse Relations Critical for Sentiment Analysis
◦ Semantic Operators Influencing Discourse Relations
 Algorithm Proposed
◦ Feature Variables
◦ Steps for different semantics operators and discourse relations
 Feature Vector Classification
 Evaluation
◦ Data Set Statistics
◦ Accuracy Measures
 Conclusion
Sentiment Analysis in Twitter with Lightweight
Discourse Analysis 2

What is Sentiment Analysis ?
 Classification on polarity of given text .
 Example : “@abc Good to see your government is working
for common people”
 Approaches : Opinion Mining

Introduction to Sentiment Analysis:
 Usage of Sentiment Analysis Applications : Brand
Monitoring , Business Planning , Risk Analysis and many
others
 Sentiment analysis is much more than simplistically
subtracting the number of “negative” words from the
number of “positive” in a document or message in order to
produce a score.

Introduction to Sentiment Analysis(Contd..) :
Fig : Approaches for designing Sentiment Analysis Application

Introduction to Sentiment Analysis (Contd..):
 Most Common Techniques Used for Sentiment Analysis:
◦ POS : Finding adjectives as they are indicators for opinion
◦ Term Presence and Frequency
◦ Opinion Dictionaries
◦ Negations : ‘not good’ is equivalent to bad
◦ Point-wise Mutual Information

Introduction to Sentiment Analysis (Contd..):
 Data expansion in recent years is one of the key factor
which increased the role of Sentiment Analysis in business.
 Looking ahead , we can see a true social democracy in
different domains which harness the opinion of crowd
rather than few experts

Motivation
▪ Example :
I’m quite excited about Tintin , despite not really liking original
comics.
 This sentence is positive although there is equal number of
positive and negative words.
 “Despite” gives more weight to previous discourse segment.

Motivation
▪ Example :
Think I’ll stay with whole sci-fi shit but this time …. classic movie

 This sentence is positive although there is equal number of
positive and negative words.
 “But” gives more weight to following discourse segment.

Motivation
 Discourse elements in text alters the polarity of text
 Sentiment Analysis classification should also take discourse
elements.
 This paper takes into consideration : connectives, modals,
conditionals and negation in a sentence for polarity
detection
 In this work bag of word model incorporates discourse
information which impacts the sentiment of a sentence

Motivation : Traditional Approach in
Discourse Analysis
• Traditional approaches for discourse analysis are mostly based on
Rhetorical Structure Theory (RTS) proposed by Mann (1988)
• But , most of these theories are focused on structured text.
• Tweet : “@abc I am gonna party 2day ….. wanna join?”
• Limitations:
• Parsers and taggers do fail frequently in unstructured text like
tweets
• Heavy linguistic processes increases the processing time and
slow down the entire application.

Introduction: Approach
 The approach deals with noisy and unstructured data as in
Tweets
 This works well even with the structured reviews and gives
a better accuracy compared to state of the art system.

Introduction: Approach
 Approach in this paper , identifies features for Sentiment
Analysis classification which incorporates discourse
information to give better sentiment classification accuracy
 Features here can include bag of words, along with domain
specific features like emoticons, hashtags, etc.

Discourse Relations
 Coherently Structured Discourse is collection of sentences related to each other.
Coherence Relation Conjunction
Cause-Effect because ; and so
Violated Expectations although; but; while
Condition If…(then) ; as long as ; while
Similarity and ; (and) similarity
Contrast by contrast ; but
Temporal Sequences (and) then ; first , second , ..before;
after ; while
Attribution according to … ; …said ; claim that …
; maintain that … ; stated
Example for example ; for instance
Elaboration also ; futhermore ; in addition
Generalization In general
Table : Contentful Conjunctions used to illustrate Coherence Relations

Discourse Relations Critical for Sentiment
Analysis
1. Violated Expectation and Contrast :
◦ Violating expectation conjunctions oppose or refute the neighboring discourse segment.
◦ Categorized in two : Conj_Fol and Conj_Prev
◦ Example :
 “He wasn’t prepared for the exam but he did pretty well”
 “I loved rides at Disney World despite having acrophobia”
◦ The discourse segment following “but” should be given more weight. In Example 2, the discourse
segment preceding “despite” should be given more weight.
2. Conclusive or Inferential Conjunctions : Set of conjunctions : Conj_infer. The discourse segment
following them should be given more weight.
Eg : @User I wasn’t much satisfied with ur so-called gud phone and subsequently decided to reject it.

Discourse Relations Critical for Sentiment
Analysis (Contd..)
3. Conditionals :
◦ The presence of “if” , tones down the final polarity as it introduces a hypothetical
situation in the context.
◦ Example : “If Micromax improved its battery life , it wud hv been gr8 phone”
4. Other Discourse Relations : Sentences having other discourse conjunctions which
has no contrasting or conflicting information can be handled by bag of word approach.

Semantic Operators Influencing Discourse
Relations
 Example : “You may like the movie despite the bad
reviews”
 Here ‘despite’ gives more weight to ‘like’
 But ‘may’ pulls down the weight as it creates uncertaininty

Relations (Contd..)
Relations Attributes
Conj_Fol but, however, nevertheless , otherwise , yet ,
still , nonetheless
Conj_Prev till , until , despite , in spite , though , although
Conj_Infer therefore , furthermore , consequently , thus ,
as a result , eventually , hence
Conditionals if
Strong_Mod might , could , can , would , may
Weak_Mod should , ought to , need not , shall , will , must
Neg not, neither , never , no , nor
Table : Discourse Relations and Semantic Operators Essential for Sentiment Analysis

Relations (Contd..)
1. Modals :
• Strong_Mod is the set of modals that express a higher degree of
uncertainity in any situation.
•Eg: He may be a rising star
• Weak_Mod is the set of modals that express lesser degree of
uncertainty and more emphasis on
certain events or situations.
Eg: Our civil service should work without interference.

Relations (Contd..)
2. Negation :
 The negation operator (Example: not, neither, nor, nothing
etc.) inverts the sentiment of the word following it.
Eg : I do not like Nokia but I like Samsung.

Algorithm Proposed :
 Discourse Relations and Semantic Operators are used to
create feature vectors
 Let user post R consist of ‘m’ sentences si(i=1…m) where
si consist of n words
wij (i=1…m , j=1..n)

Algorithm Proposed (Cond..): Feature
Variables
 Let A be the set of all discourse relations as described
before.
 Feature Variables :
 fij : Weight of the word in sentence , initialized to 1
 flipij : Indicates weather the polarity of word should be flipped or not
 hypij : Indicates the presence of conditional or strong modal in sentence
 Iterate the algorithm for every sentence :
for i = 1 … m

Algorithm Proposed (Cond..): Step 1
 Deals with conditional and strong modals
 Algo :
 for j =1..n
hypij = 0;
if wij == Conditional OR Strong _Mod :
hypij = 1;
end if
end for

 Deals with Conj_Fol and Conj_Infer
 Algo :
 for j =1..n
fij = 1;
if wij == Conj_Fol OR Conj_Infer :
for k=j+1 .. N and w != A
fik + = 1;
end for
end if
end for

 Deals with Conj_Prev
 Algo :
 for j =1..n
fij = Taken from Step 2;
if wij == Conj_Prev :
for k=1 .. j-1 and w != A
fik + = 1;
end for
end if
end for

 Deals with Negation
 Algo :
 for j =1..n
flipij = 1;
if wij == Neg :
for k=1 .. Neg_Window and w != (Conj_Prev OR Conj_Fol)
flipi,j+k = -1;
end for
end if
end for

Feature Vector Classification
 We devised a lexicon based
system as well as a supervised
system for feature vector
 Lexicon Based Classification :
◦ Here :
 fij : weight of each word in sentence
 flipij : indicates polarity of word should
be flipped or not
 hypij : indicates the presence of
conditional or strong_mod

Feature Vector Classification (Contd..)
 Supervised Classification :
◦ SVM’s are used to classify the set of feature vectors {flipij, wij, fij
and hypij}.
◦ Features used :
 N-grams
 Stop Words
 Feature Weight
 Modal and Conditional Indicator
 Stemming
 Negation
 Emoticons
 POS
 Feature Space

Evaluation : Data Sets Statistics
 Data Set 1 :
◦ 8507 tweets are collected based on a total of around 2000 different entities from
20 different domains.
◦ Four classes - positive, negative, objective-not-spam and objective-spam.
 Data Set 2 :
◦ Hashtags #positive, #joy, #excited, #happy etc. are used to collect tweets
bearing positive sentiment,
◦ Hashtags like #negative, #sad, #depressed, #gloomy, #disappointed etc. are
used to collect negative tweets.
 Data Set 3 :
◦ Travel Review Dataset contains 595 polarity-tagged documents for each of the
positive and negative classes.

Evaluation : Data Sets Statistics
 Base Line System : C-Feel-It (Joshi et al., 2011) Rule Based System
 The only difference between the two systems is the handling of
connectives , modals, conditionals and negation
Manually Annotated DataSet 1
#Positive #Negative #ObjectiveNotSpam #Objective #Total
2548 1209 2757 1993 8507
Auto Annotated DataSet 2
#Positive #Negative Total
7348 7866 15214
Table : Twitter Datasets 1 and 2 Statistics

Evaluation : Accuracy Measures
Table : Accuracy Comparision b/w Base Line System and Discourse System using Lexicon in Data Set 1 and 2
Accuracy Measure for 2 Class Classification : DataSet 1
Base Line System Discourse System
68.58 72.81
57.2 61.31
80.55 84.91

Fig : Accuracy Comparision b/w Base Line System and Discourse System using Lexicon in Data Set 1 and 2
0
10
20
30
40
50
60
70
80
90
Accuracy Measure for 2 Class Classification
: DataSet 1
: DataSet 1
: DataSet 2
BaseLineSystem
Discourse System

Table : Accuracy Comparison b/w Base Line SVM System and Discourse SVM System in Data Set 1 and 2
69.49 70.75
63.11 64.23
91.99 93.01

Fig : Accuracy Comparison b/w Base Line SVM System and Discourse SVM System in Data Set 1 and 2
0
10
20
30
40
50
60
70
80
90
100
: DataSet 1
: DataSet 1
: DataSet 2
BaseLineSystem
Discourse System

•This evaluation is used to determine whether our discourse based approach
performs well for structured text as well
Sentiment Evaluation Criterion Accuracy
Baseline Bag-of-Words Model 69.62
Bag-of-Words Model + Discourse 71.78
Table : Accuracy Comparision between Bag of Word and Siscourse System using Lexicon in DataSet 3

 Supervised Classification Approach under 4 scenarios :
◦ When only unigrams are used
◦ When only sense of unigrams are used
◦ When unigrams are used along with their senses (synset-id’s)
◦ When unigrams are used with senses and discourse features
Discourse Analysis 37Note : IWSD : Iterative Word Sense Disambiguation ((Khapra et al., 2010)
System Accuracy(%)
Only Unigrams 84.9
Only IWSD Sense of Unigrams 85.48
Unigrams + IWSD Sense of Unigrams 86.08
Unigrams + IWSD Sense of Unigrams +
Discourse 88.13
Table : Accuracy Comparison In Dataset 3

Discourse Analysis 38Note : IWSD : Iterative Word Sense Disambiguation ((Khapra et al., 2010)
Figure: Accuracy Comparison In Dataset 3
84.9
85.48
86.08
88.13
83
84
85
86
87
88
89
Unigram IWSD Sense of Unigram Unigram + IWSD Sense of Unigram Unigram + IWSD Sense of Unigram +
Discourse
Accuracy(%)

Conclusion
 Most of the works on unstructured data uses bag of words
approach , ignores discourse markers
 Evaluation on different dataset show that incorporating
discourse information improves the performance by 2-4%
 The approach presented can deal with unstructured data as
it doesn’t need to parse the document.

Questions for You
 What feature variables are used in the algorithm for
incorporating discourse information?
 What is the advantage of the presented approach over
traditional approaches ?

Sentiment Analysis in Twitter with Lightweight Discourse Analysis

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