The document discusses various frameworks and methods for Persian sentiment analysis, including feature selection, machine learning, and lexicon-based approaches. It highlights the evaluation of sentiment analysis methods using datasets and reports on specific studies analyzing political sentiment in Farsi tweets. Additionally, it addresses challenges such as normalization, spell correction, and email extraction in the context of Farsi language processing.

1. Persian Sentiment Analysis
Natural Language Processing
Moein Hosseini
abc@moein.xyz

2. Outline
● Introduction
● Researches
– A 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
● Usages
– Analyzing the Political Sentiment of Tweets in Farsi
● Data Sets

3. Introduction

4. Different Levels of Analysis
● Document level
● Sentence level
● Entity and Aspect level
– Opinion: (e, a, s, h, t) →
("mac pro", "openness", -10, "Moein", 1464582

5. Different Types of Opinions
● Regular vs Comprative
● Explicit vs Implicit

6. Sentiment Analysis Approaches
● Machine Learning Approach
– Identify non-sentiment terms, implied sentiment
– Need Seed Data, Domain Dependency
● Lexicon Based Approach
– Word Net, Senti Word Net

7. Persian Sentiment Analysis

8. A Framework for Sentiment
Analysis in Persian
Published in:
Open Transactions on Information Processing
Authors:
● Basiri, Mohammad
● Nilchi, Ahmad
● Ghassem-Aghaee, Nasser

9. A Framework for Sentiment
Analysis in Persian

10. A Framework for Sentiment
Analysis in Persian
Normalization: Solve Basic Challenges
– Different forms of writing:
– Different Unicode:
– Space and Psudo-Space:

11. A Framework for Sentiment
Analysis in Persian
Spell Correction:
● Many alphabets for one sound:
– can be written in 48 ways
● Informal words
–

12. A Framework for Sentiment
Analysis in Persian
Stemmer: Using Dolamic Stemmer
● Remove stop words
● Doesn't affect verbs
● But most of sentimet words are
related to Nouns and Adjectives

13. A Framework for Sentiment
Analysis in Persian
Sentence Splitting: Any comment
● Unit of text
● Collection of sentences

14. A Framework for Sentiment
Analysis in Persian
Polarity Detection: Translated SentiStrength

15. A Framework for Sentiment
Analysis in Persian
Aggregation:
● SentiStrength
● Maximum of scores
● Scaled rate
● Sum of maximums
● Dempster-Shafer

16. A Framework for Sentiment
Analysis in Persian
Dempster-Shafer:

17. A Framework for Sentiment
Analysis in Persian
Evaluation:
● mobile.ir
● Number of reviews: 1100
●
Avrage number of words: 2547
●
Avrage number of sentence: 191

18. A Framework for Sentiment
Analysis in Persian
Result:

19. A non-parametric LDA-based
induction method for sentiment
analysis
Published in:
AISP 2012 - 16th CSI International Symposium on
Artificial Intelligence and Signal Processing
Authors:
● Shams, Mohammadreza
● Shakery, Azadeh
● Faili, Heshaam

20. LDASA
● Build Persian Clues:
– Translate English lexicon to Persian
– Correct errores
● LDA
● Classification

21. LDASA
Translate English lexicon to Persian
● Subjectivly Clues (8027 terms)
● Using automatically translation
– So differente size:
● Jelouse: negative
–
–
● Reduce Size
– Remove frequent & infrequent words

22. LDASA
Error Correction:
● Using word net
– There is no well defined Persian word net
● Using concept graph
– Comments are too small for that
● Using mutual information
– Again LIKE A BOSS

23. 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

24. LDASA
● Topic Extraction: LDA
● Classification:
– Positive and Negative
● Evaluation:
– Phones, digital cameras, hotels
– 200 positive and 200 negative for each group

25. LDASA
Evaluation:

26. Feature selection methods in
Persian sentiment analysis
Published in:
Natural Language Processing and Information
Authors:
● Saraee, Mohamad
● Bagheri, Ayoub

27. Feature selection methods in
Persian sentiment analysis
● Feature Selection for Sentiment Analysis
– Document Frequency (DF)
– Term Frequency Variance (TFV)
– Mutual Information (MI)
– Modified Mutual Information (MMI)

28. Feature selection methods in
Persian sentiment analysis
● Mutual Information:
c1
c2
f1
A B
f2
C D

29. Feature selection methods in
Persian sentiment analysis
● Mutual Information:
c1
c2
f1
A B
f2
C D

30. Feature selection methods in
Persian sentiment analysis
● Evaluation:

31. Emotions from Farsi Texts with
Mutual-Word- Counting and Word-
Spotting
Published in:
The 16th CSI International Symposium on
Artificial Intelligence and Signal Processing
Authors:
● Jahromi, Amir Namvar
● Homayounpour, Mohammad Mehdi

32. Emotions from Farsi Texts with
Mutual-Word- Counting and Word-
Spotting
● Sentiment:
– Polarity: Positive, Negative
● Sense:
– Happy, Sad, Angry and ...

33. Emotions from Farsi Texts with
Mutual-Word- Counting and Word-
Spotting
Sensing Methods:
●
Word Count
– Counting
– Weighted Counting
●
Word Spotting
– Labeled 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 Count
– Two similar words are counted as single word
●
Mutual Word Count And Word Spotting

34. Emotions from Farsi Texts with
Mutual-Word- Counting and Word-
Spotting
Evaluation:
● 2243 sentences in four group: happy, neutral,
sad, angry

35. 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

36. Others
● A SVM-based method for sentiment analysis
in Persian language
●‫یییی‬ ‫یییییی‬ ‫ییی‬ ‫ییییی‬
‫ییییی‬ ‫یییی‬ ‫یی‬ ‫ییییییی‬
‫ی‬ ‫ییییییی‬ ‫یی‬ ‫ییییییی‬ ‫یی‬
‫یییییییی‬SVM

37. Usages

38. 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

39. Analyzing the Political Sentiment of
Tweets in Farsi
● Using Twitter Steam API During Iran Deal Negotiation
● Filtering by some terms:
–
–
–
–
–
–
–
–
– ...

40. Analyzing the Political Sentiment of
Tweets in Farsi
● 3000 tweets labeled by native persian
Speakers
– 1,2 → negative → 37%
– 3 → neutral → 35%
– 4,5 → positive → 27%
● Using Brown
● SVM
– 1000 clusters + 3 as cutoff

41. Analyzing the Political Sentiment of
Tweets in Farsi
● Sub Topic By LDA

42. Analyzing the Political Sentiment of
Tweets in Farsi
● Result

43. Data Sets

44. Persian SentiWordNet
Adjectives: Manualy Annoutation
● Positive: 968 words
● Negative: 962 words
● Neutral: 1572 words

45. Persian SentiWordNet
Adjectives + Verbs + Nouns: Semi-Supervised
● Adjectives: 3588 words
● Verbs: 4073 words
● Nouns: 7325 words

46. Persian SentiWordNet
Semi-supervised word polarity identification in
resource-lean languages
Authors:
● Iman Dehdarbehbahania
● Azadeh Shakerya
● Heshaam Failia

47. Others
●
●‫ییییییییی‬ ‫ییییییی‬ ‫یییی‬ :‫یییییی‬
‫یییییی‬
●‫ییی‬‫ی‬‫یییی‬‫ی‬‫ییییی‬ ‫ییییی‬ ‫ییییی‬ ‫ییییی‬
●‫یییی‬ ‫یییییی‬ ‫ییییی‬ ‫یییی‬ ‫یییییی‬
) ‫ییییی‬Persian ESD(

48. Thanks for your attention