online debate summarization using topic directed sentiment analysis

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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

+0

+0

+0 +1 +0 +1

+Word Sentiment Score �

n Updating word sentiment score. �

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+0

+0

+0 +1 +0 +1

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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 �v123582@gmail.com�