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