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The results produced via machine learning techniques are quite good in comparison to the humangenerated baselines discussed in Section 4

Thumbs up?: sentiment classification using machine learning techniques

empirical methods in natural language processing, (2002): 79-86

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摘要

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...更多

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简介
  • Very large amounts of information are available in on-line documents. As part of the effort to better organize this information for users, researchers have been actively investigating the problem of automatic text categorization.

    The bulk of such work has focused on topical categorization, attempting to sort documents according to their subject matter (e.g., sports vs. politics).
  • Recent years have seen rapid growth in on-line discussion groups and review sites where a crucial characteristic of the posted articles is their sentiment, or overall opinion towards the subject matter — for example, whether a product review is positive or negative
  • Labeling these articles with their sentiment would provide succinct summaries to readers; these labels are part of the appeal and value-add of such sites as www.rottentomatoes.com, which both labels movie reviews that do not contain explicit rating indicators and normalizes the different rating schemes that individual reviewers use.
  • There are potential applications to message filtering; for example, one might be able to use sentiment information to recognize and discard “flames”(Spertus, 1997)
重点内容
  • Today, very large amounts of information are available in on-line documents
  • The bulk of such work has focused on topical categorization, attempting to sort documents according to their subject matter
  • Sentiment classification would be helpful in business intelligence applications (e.g. MindfulEye’s Lexant system1) and recommender systems (e.g., Terveen et al (1997), Tatemura (2000)), where user input and feedback could be quickly summarized; in general, free-form survey responses given in natural language format could be processed using sentiment categorization
  • We examine the effectiveness of applying machine learning techniques to the sentiment classification problem
  • The results produced via machine learning techniques are quite good in comparison to the humangenerated baselines discussed in Section 4
  • Though, the superiority of presence information in comparison to frequency information in our setting contradicts previous observations made in topic-classification work (McCallum and Nigam, 1998)
结果
  • Initial unigram results The classification accuracies resulting from using only unigrams as features are shown in line (1) of Figure 3.
  • In topic-based classification, all three classifiers have been reported to use bagof-unigram features to achieve accuracies of 90% and above for particular categories (Joachims, 1998; Nigam et al, 1999)9 — and such results are for settings with more than two classes.
  • This provides suggestive evidence that sentiment categorization is more difficult than topic classification, which corresponds to the intuitions of the text categorization expert mentioned above.10 the authors still wanted to investigate ways to improve the sentiment categorization results; these experiments are reported below
结论
  • The results produced via machine learning techniques are quite good in comparison to the humangenerated baselines discussed in Section 4.
  • Though, the superiority of presence information in comparison to frequency information in the setting contradicts previous observations made in topic-classification work (McCallum and Nigam, 1998).
  • What accounts for these two differences — difficulty and types of information proving useful — between topic and sentiment classification, and how might the authors improve the latter?
  • What accounts for these two differences — difficulty and types of information proving useful — between topic and sentiment classification, and how might the authors improve the latter? To answer these questions, the authors examined the data further. (All examples below are drawn from the full 2053-document corpus.)
基金
  • This paper is based upon work supported in part by the National Science Foundation under ITR/IM grant IIS0081334
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