Online debate summarization using topic directed sentiment analysis @ WISDOM'13

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Online Debate Summarization Using Topic
Directed Sentiment Analysis
Sarvesh Ranade, Jayant Gupta, Vasudeva Varma, Radhika Mamidi
2014/10/31 (Fri.)
WISDOM ‘13
Chang Wei-Yuan @ MakeLab Group Meeting

2. +Outline
n Introduction
n Method
n Experiment
n Conclusion
n Thought
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3. +Outline
n Introduction
n Method
n Experiment
n Conclusion
n Thought
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4. +Introduction
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5. +Introduction
n Online debate forum
n take a stance and argue debate topics
n dynamic and increase rapidly
n This paper aims to summarize online
debates.
n extracting highly topic relevant
n sentiment rich sentences
n Effective opinion summarization without
going through the entire debate.
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6. +Outline
n Introduction
n Method
n Experiment
n Conclusion
n Thought
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7. +Method
n Extractive summaries are generated by
ranking the Dialogue Acts (DAs) from
the original documents.
n DA is a smallest unit of debate
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8. + 8
Score
Score
Score
Score
Score

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n Most highly ranked DAs are chosen until
summary length constraint is satisfied.
n Scores of DAs
n
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Equation.（Scores of DAs）
where λ is weighted, s is a DA of the Document, D is
the Document.

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Feature Category
Feature Names
Topic Relevance Topic Directed Sentiment Score
Topic Co-occurrence
Document Relevance
tf-idf Sentiment Score
Sentiment Relevance
Number of Sentiment Words
Sentiment Strength
Context Relevance
Sentence position
Sentence length
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n Argument Structure Examples

11. +Word Sentiment Score
n Word Sentiment Score
n Parsing dependency1 parse of the DAs.
n Calculating word sentiment score2.
n Updating word sentiment score.
n good student, great warrior
1Stanford dependency parse http://nlp.stanford.edu:8080/parser/
2Sentiment lexicon SentiWordNet http://sentiwordnet.isti.cnr.it/
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12. +Word Sentiment Score
n Parsing dependency parse of the DAs.
n “ A large company needs a sustainable
business model. ”
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13. +Word Sentiment Score
n Calculating word sentiment score.
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+0
+0
+0
+0
+1+1
+0
+0

14. +Word Sentiment Score
n Updating word sentiment score.
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+0
+0
+1
+1
+1+1
+0
+0

15. +Extended Targets
n Extended Targets (ET)
n Extended targets are the entities closely
related to debate topics.
n To extract the extended targets, we capture
named entities (NE) from Wikipedia page of
the debate topic.
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16. +Topic Relevance
n Topic Directed Sentiment Score
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Equation.（Topic Directed Sentiment Score）
where w is a word in DA, ET() is Extended Targets.

17. +Topic Relevance
n Topic Co-occurrence
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Equation.（Topic Co-occurrence Score）

18. +Document Relevance
n tf-idf Sentiment Score
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Equation.（Topic Co-occurrence Score）

19. +Sentiment Relevance
n Number of Sentiment Words
n
n Sentiment Strength
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Equation.（Sentiment Strength Score）

20. +Context Relevance
n Sentence position
n In debates, initial and ending DAs of the
debate posts are more important than the
middle ones.
n Sentence length
n As the longer sentences tend to contain more
information, we have used sentence length as
document context feature.
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21. +Method
n Scores of DAs
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Equation.（Scores of DAs）
where λ is weighted, s is a DA of the Document, D is
the Document.

22. +Outline
n Introduction
n Method
n Experiment
n Conclusion
n Thought
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23. +Experiment
n This paper extracted 10 online debate
discussions from www.convinceme.net.
n Number of users：1168
n Number of posts ：1945
n Number of DA：23681
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24. +Experiment
n Following values gave the best results
as indicated by ROUGE results by grid
search.
n λtopicRel = 0.3
n λdocRel = 0.1
n λsentiRel = 0.5
n λconRel = 0.1
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25. +Experiment
n We compared our system to the
following systems：
n Max-length
n Lead
n pHAL
n tf-Idf
n OpinionSumm
n document similarity, topic relevance, sentiment and
length
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26. +Result
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27. +Result
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28. +Outline
n Introduction
n Method
n Experiment
n Conclusion
n Thought
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29. +Conclusion
n This paper focuses on summarizing the
on-line debates.
n topic directed sentiment
n topic related information
n The results show that our system beats
all these systems comprehensively.
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30. +Future Work
n Sentiment scores
n word sense disambiguation
n domain specific sentiment analysis
n Creating users' profile by capturing their
intention.
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31. +Outline
n Introduction
n Method
n Experiment
n Conclusion
n Thought
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Thought Debate Comparison
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33. +Introduction
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Thanks for listening.
2014 / 10 / 31 (Fri.) @ MakeLab Group Meeting
v123582@gmail.com