Persian Sentiment Analysis

Natural Language Processing

Moein Hosseiniabc@moein.xyz

Outline

Introduction

ResearchesA Framework for Sentiment Analysis in Persian

A Non-Parametric LDA-Based Induction Method for Sentiment Analysis

Feature Selection Methods in Persian Sentiment Analysis

Emotions from Farsi Texts with Mutual-Words-Counting and Word-Spotting

Others

UsagesAnalyzing the Political Sentiment of Tweets in Farsi

Data Sets

Introduction

Different Levels of Analysis

Document level

Sentence level

Entity and Aspect levelOpinion: (e, a, s, h, t)
("mac pro", "openness", -10, "Moein", 1464582

Different Types of Opinions

Regular vs Comprative

Explicit vs Implicit

Sentiment Analysis Approaches

Machine Learning ApproachIdentify non-sentiment terms, implied sentiment

Need Seed Data, Domain Dependency

Lexicon Based ApproachWord Net, Senti Word Net

Persian Sentiment Analysis

A Framework for Sentiment Analysis in Persian

Published in: Open Transactions on Information Processing Authors:Basiri, Mohammad

Nilchi, Ahmad

Ghassem-Aghaee, Nasser

A Framework for Sentiment Analysis in Persian

A Framework for Sentiment Analysis in Persian

Normalization: Solve Basic ChallengesDifferent forms of writing:

Different Unicode:

Space and Psudo-Space:

A Framework for Sentiment Analysis in Persian

Spell Correction:Many alphabets for one sound: can be written in 48 ways

Informal words->

A Framework for Sentiment Analysis in Persian

Stemmer: Using Dolamic StemmerRemove stop words

Doesn't affect verbs

But most of sentimet words are
related to Nouns and Adjectives

A Framework for Sentiment Analysis in Persian

Sentence Splitting: Any commentUnit of text

Collection of sentences

A Framework for Sentiment Analysis in Persian

Polarity Detection: Translated SentiStrength

A Framework for Sentiment Analysis in Persian

Aggregation:SentiStrength

Maximum of scores

Scaled rate

Sum of maximums

Dempster-Shafer

A Framework for Sentiment Analysis in Persian

Dempster-Shafer:

A Framework for Sentiment Analysis in Persian

Evaluation:mobile.ir

Number of reviews: 1100

Avrage number of words: 2547

Avrage number of sentence: 191

A Framework for Sentiment Analysis in Persian

Result:

A non-parametric LDA-based induction method for sentiment analysis

Published in: AISP 2012 - 16th CSI International Symposium on Artificial Intelligence and Signal ProcessingAuthors:Shams, Mohammadreza

Shakery, Azadeh

Faili, Heshaam

LDASA

Build Persian Clues:Translate English lexicon to Persian

Correct errores

LDA

Classification

LDASA

Translate English lexicon to PersianSubjectivly Clues (8027 terms)

Using automatically translationSo differente size:Jelouse: negative

Reduce SizeRemove frequent & infrequent words

LDASA

Error Correction:Using word netThere is no well defined Persian word net

Using concept graphComments are too small for that

Using mutual informationAgain LIKE A BOSS

LDASA

Mutual Information:

Iterative task runs to correct errors:Seed and init: 40 most used positive

40 most used negative

Correct one word polarity in each interation

LDASA

Topic Extraction: LDA

Classification:Positive and Negative

Evaluation:Phones, digital cameras, hotels

200 positive and 200 negative for each group

LDASA

Evaluation:

Feature selection methods in Persian sentiment analysis

Published in: Natural Language Processing and InformationAuthors:Saraee, Mohamad

Bagheri, Ayoub

Feature selection methods in Persian sentiment analysis

Feature Selection for Sentiment AnalysisDocument Frequency (DF)

Term Frequency Variance (TFV)

Mutual Information (MI)

Modified Mutual Information (MMI)

Feature selection methods in Persian sentiment analysis

Mutual Information:

c1c2

f1AB

f2CD

Feature selection methods in Persian sentiment analysis

Mutual Information:

c1c2

f1AB

f2CD

Feature selection methods in Persian sentiment analysis

Evaluation:

Emotions from Farsi Texts with Mutual-Word- Counting and Word-Spotting

Published in: The 16th CSI International Symposium on Artificial Intelligence and Signal ProcessingAuthors:Jahromi, Amir Namvar

Homayounpour, Mohammad Mehdi

Emotions from Farsi Texts with Mutual-Word- Counting and Word-Spotting

Sentiment:Polarity: Positive, Negative

Sense: Happy, Sad, Angry and ...

Emotions from Farsi Texts with Mutual-Word- Counting and Word-Spotting

Sensing Methods:Word CountCounting

Weighted Counting

Word SpottingLabeled Word: if the words of more than one emotion exists in the sentence, the emotion with more number of related words is selected as a final result

Mutual Word CountTwo similar words are counted as single word

Mutual Word Count And Word Spotting

Emotions from Farsi Texts with Mutual-Word- Counting and Word-Spotting

Evaluation:2243 sentences in four group: happy, neutral, sad, angry

Others

Opinion Mining in Persian Language Using Supervised Algorithms

Lexicon-based sentiment analysis for Persian text

Sentiment classification in Persian: Introducing a mutual information-based method for feature selection

Others

A SVM-based method for sentiment analysis in Persian language

SVM

Usages

Analyzing the Political Sentiment of Tweets in Farsi

Published in: Proceedings of the Tenth International AAAI Conference on Web and Social Media (ICWSM 2016)Authors:Vaziripour, Elham

Zappala, Daniel

Giraud-carrier, Christophe

Analyzing the Political Sentiment of Tweets in Farsi

Using Twitter Steam API During Iran Deal Negotiation

Filtering by some terms:

...

Analyzing the Political Sentiment of Tweets in Farsi

3000 tweets labeled by native persian Speakers1,2 negative 37%

3 neutral 35%

4,5 positive 27%

Using Brown

SVM1000 clusters + 3 as cutoff

Analyzing the Political Sentiment of Tweets in Farsi

Sub Topic By LDA

Analyzing the Political Sentiment of Tweets in Farsi

Result

Data Sets

Persian SentiWordNet

Adjectives: Manualy AnnoutationPositive: 968 words

Negative: 962 words

Neutral: 1572 words

Persian SentiWordNet

Adjectives + Verbs + Nouns: Semi-SupervisedAdjectives: 3588 words

Verbs: 4073 words

Nouns: 7325 words

Persian SentiWordNet

Semi-supervised word polarity identification in resource-lean languagesAuthors:Iman Dehdarbehbahania

Azadeh Shakerya

Heshaam Failia

Others

:

(Persian ESD)

Thanks for your attention