online debate summarization using topic directed sentiment analysis
DESCRIPTION
Online debate summarization using topic directed sentiment analysis @ WISDOM'13TRANSCRIPT
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Online Debate Summarization Using Topic Directed Sentiment Analysis
2014/10/31 (Fri.)�Chang Wei-Yuan @ MakeLab Group Meeting
Sarvesh Ranade, Jayant Gupta, Vasudeva Varma, Radhika Mamidi �WISDOM ‘13
+Outline
n Introduction �
n Method �
n Experiment �
n Conclusion �
n Thought
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+Outline
n Introduction �
n Method �
n Experiment �
n Conclusion �
n Thought
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+ Introduction 4
+ 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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+Outline
n Introduction �
n Method �
n Experiment �
n Conclusion �
n Thought
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+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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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
+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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+Word Sentiment Score �
n Parsing dependency parse of the DAs. �n “ A large company needs a sustainable
business model. ”
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+Word Sentiment Score �
n Calculating word sentiment score. �
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+0 +1 +0 +1
+Word Sentiment Score �
n Updating word sentiment score. �
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+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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+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.
+Topic Relevance
n Topic Co-occurrence �
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Equation.（Topic Co-occurrence Score）�
+Document Relevance
n tf-idf Sentiment Score �
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Equation.（Topic Co-occurrence Score）�
+Sentiment Relevance �
n Number of Sentiment Words �n �
n Sentiment Strength
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Equation.（Sentiment Strength Score）�
+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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+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.
+Outline
n Introduction �
n Method �
n Experiment �
n Conclusion �
n Thought
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+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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+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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+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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+Result 26
+Result 27
+Outline
n Introduction �
n Method �
n Experiment �
n Conclusion �
n Thought
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+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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+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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+Outline
n Introduction �
n Method �
n Experiment �
n Conclusion �
n Thought
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Thought Debate Comparison
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+ Introduction 33
+Thanks for listening. 2014 / 10 / 31 (Fri.) @ MakeLab Group Meeting �[email protected]�