Thumbs up? Sentiment Classification using Machine Learning Techniques

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Thumbs up? Sentiment Classification using Machine Learning Techniques (2002)

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by Bo Pang , Lillian Lee , Shivakumar Vaithyanathan

Venue:	IN PROCEEDINGS OF EMNLP
Citations:	1101 - 7 self

BibTeX

@INPROCEEDINGS{Pang02thumbsup?,
    author = {Bo Pang and Lillian Lee and Shivakumar Vaithyanathan},
    title = {Thumbs up? Sentiment Classification using Machine Learning Techniques},
    booktitle = {IN PROCEEDINGS OF EMNLP},
    year = {2002},
    pages = {79--86},
    publisher = {}
}

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Abstract

We consider the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative. Using movie reviews as data, we find that standard machine learning techniques definitively outperform human-produced baselines. However, the three machine learning methods we employed (Naive Bayes, maximum entropy classification, and support vector machines) do not perform as well on sentiment classification as on traditional topic-based categorization. We conclude by examining factors that make the sentiment classification problem more challenging. 1

Keyphrases

sentiment classification machine learning technique support vector machine standard machine movie review naive bayes human-produced baseline sentiment classification problem overall sentiment maximum entropy classification traditional topic-based categorization

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