sentiment analysis

1. Sentiment analysis on twitter
Presenter
NITHISH J PRABHU
4JN12IS066
Information Science & Engineering
Guided By
Mrs. G. V. SOWMYA
Assistant Professor
Information Science & Engineering

2. CONTENT
 INTRODUCTION
 WHY TWITTER
 SYSTEM ARCHITECTURE
 SCORING MODULE
 SENTIMENT SCORING
 CONCLUSION

3. INTRODUCTION
Understanding people is difficult.
Sentimental Analysis involves user’s attitude towards
particular topic
-- positive
-- negative
-- neutral

4. WHY NEEDED ?
• Promotion: is this review positive or negative?
• Products: what do people think about the new iPhone?
• Politics: what do people think about this candidate or issue?
• Prediction: predict election outcomes or market trends from
sentiment

5. MICROBLOGGING

6. TWITTER
Message Length: Tweets message is 140 characters.
Writing technique: The occurrence of incorrect spellings and cyber
slang.
Availability: The amount of data available is immense.
Topics: Twitter users post messages about a range of topics.

7. TWITTER TERMINOLOGY
tweet re-tweet mention trends

9. User Tweet Twitter API
Removal of URL,
@tags, #tags
Spell
Correction
Emoticon
Tagger
POS Tagger
Transaction File
• Emoticons
• Adjective
• Adverb
Scoring Module
• Corpus Based
• Dictionary Based
Tweet
Sentiment
Score
SYSTEM ARCHITECTURE

10. EMOTICONS STRENGTH

11. SCORING MODULE
 Corpus Based Approach – Adjective
 Dictionary Based Approach – Verb & Adverb

12. CORPUS BASED APPROACH
 Adjective used to qualify object and domain specific.
 But conjoined adjective makes situation reverse.
Example: Honest ‘and’ peaceful – same orientation
Talented ‘but’ Irresponsible – opposite orientation

13. CORPUS BASED APPROACH
 Log Linear Regression Model with Linear Predictor
where X is Conjunction counts
W is Weight vector
 Similarity between is calculated by
Seed List are taken & Semantic scores will be assigned.

14. DICTIONARY BASED APPROACH
 Adverb can also change meaning of Adjective.
Example: This is not a good book;
Verb can also convey opinions.
Example: love, hate;
Semantic orientation is calculated by Word Net &
added to Seed List.

15. VERB & ADVERB STRENGTH

16. DICTIONARY BASED APPROACH - ALGORITHM

17. TWEET SENTIMENT SCORING
To calculate the overall sentiment of the tweet, average the
strength of all opinion indicators as

18. EXAMPLE
Fraction of tweet in caps: BOOOORING
Pc=1/18=0.055
Length of repeated sequence, BOOOORING,
Ns=3
Number of Exclamation marks, !!!,
Nx=3

19. EXAMPLE
The list of Adjective Groups:
AG1=totally unprepared, AG2=not good, AG3=boring
The list of Verb Groups:
VG1=hate
The list of Emoticons:
E1 = :(, Ne1 = 2

20. EXAMPLE
Score of Adjective Group
S (AG1) = S (totally unprepared) =0.8*-0.5 == -0.4
S (AG2) = S (not good) =-0.8*1= -0.8
S (AG3) = S (boring) = 0.5*-0.25 = -0.125
 Score of Verb Group
S (VG1) = S (hate) = 0.5*-1 = -0.5

21. TWEET SENTIMENT SCORING
Since, the score is Negative value, Tweet is considered as
Negative tweet

22. FREQUENCY OF POSITIVE & NEGATIVE TWEETS

23. CONCLUSION
 The proliferation of microblogging sites like Twitter offers
an opportunity to create theories & technologies that mine
for opinions.
 Corpus Based & Dictionary Based approach help to find
semantic orientation.
 Better the understand, better the move.

24. ANY QUERIES

25. Thank you