긍정/부정 비대칭도를 이용한 소수상품평의 검색
Retrieving Minority Product Reviews Using Positive/Negative Skewness
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참고문헌
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초록
소수 의견을 포함하는 온라인 상품평은 긍정 또는 부정 일변도인 상품평에서는 찾기 어려운 유익한 정보를 내포하기도 한다. 본 논문에서는 주어진 상품평 집합 속에서 소수상품평을 검색하는 방법을 제안한다. 제안방법은 개별 상품평을 먼저 긍정/부정 상품평으로 자동분류한 뒤, 주어진 상품평 집합의 긍정/부정 상품평의 비대칭도를 계산하여 소수상품평을 검색한다. 소수상품평 검색에서는 긍정/부정 자동분류성능이 소수상품평 검색성능에 영향을 주는데, 본 논문에서는 도메인에 특화된 감성사전과 그렇지 않은 일반적인 감성사전을 가지고 상품평을 긍정/부정으로 감성분류한 뒤 비대칭도를 계산하여 소수상품평 검색성능을 비교한다. 스마트폰과 영화를 다룬 온라인 영문 상품평에대하여 도메인에 특화된 감성사전을 가지고 소수상품평 검색성능을 평가한 결과, F1점수는 각각 24.6%와 15.9%였고, 정확도는 각각 56.8%와 46.8%였다. 이는 스마트폰과 영화의 개별 상품평 긍정/부정 분류 정확도가 각각 85.3%와 78.8%일 때의 성능이다. 본 논문에서는 또 긍정/부정 자동분류 성능이 주어졌을 때의 이론적인 소수상품평 검색성능에 대해서도 논의한다.
Abstract
A given product’s online product reviews build up to form largely positive or negative reviews or mixed reviews that include both the positive and negative reviews. While the homogeneously positive or negative reviews help readers identify the generally praised or criticized product, the mixed reviews with minority opinions potentially contain valuable information about the product. We present a method of retrieving minority opinions from the online product reviews using the skewness of positive/negative reviews. The proposed method first classifies the positive/negative product reviews using a sentiment dictionary and then calculates the skewness of the classified results to identify minority reviews. Minority review retrieval experiments were conducted on smartphone and movie reviews, and the F1-measures were 24.6% (smartphone) and 15.9% (movie) and the accuracies were 56.8% and 46.8% when the individual reviews’ sentiment classification accuracies were 85.3% and 78.8%. The theoretical performance of minority review retrieval is also discussed.
참고문헌 (17)
- S. M. Mudambi, "What makes a helpful online review? A study of customer reviews on Amazon. com", MIS Quarterly, 34(1), pp.185~200 (2010)
- Z. Zhang, "Utility scoring of product reviews", Proceedings of the 15th ACM International Conference on Information and Knowledge Management, pp.51~57 (2006)
- B. Pang, "Opinion mining and sentiment analysis", Foundations and Trends in Information Retrieval, 2(1-2), pp.1~135 (2008)
- H. Cho, "Data-driven integration of multiple sentiment dictionaries for lexicon-based sentiment classification of product reviews", Knowledge-Based Systems, 71, pp.61~71 (2014)
- D. P. Doane, "Measuring skewness: A forgotten statistic?", Journal of Statistics Education, 19(2), pp.1~18 (2011)
- M. G. Bulmer, "Principles of Statistics" (1979), Dover
- F. Å. Nielsen, "A new ANEW: Evaluation of a word list for sentiment analysis in microblogs", Proceedings of ESWC Workshop Making Sense of Microposts (2011)
- Affective Norms for English Words (ANEW): Instruction manual and affective ratings, "M. Bradley", The Center for Research in Psychophysiology, University of Florida, ~, pp.Technical Report C-1 (1999)
- P. J. Stone, "A computer approach to content analysis: Studies using the General Inquirer system", Proceedings of Spring Joint Computer Conference of American Federation of Information Processing Societies (1963)
- S. Cerini, "Language Resources and Linguistic Theory" (2007), iFranco Angeli
- M. Hu, "Mining and summarizing customer reviews", Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp.168~177 (2004)
- E. Cambria, "SenticNet 2 : A semantic and affective resource for opinion mining and sentiment analysis", Proceedings of the International Conference on Florida Artificial Intelligence Research Society, pp.202~207 (2012)
- J. C. de Albornoz, "SentiSense : Aneasily scalable concept-based affective lexicon for sentiment analysis", Proceedings of the 8th International Conference on Language Resources and Evaluation, pp.3562~3567 (2012)
- S. Baccianella, "SentiWordNet 3. 0 : An enhanced lexical resource for sentiment analysis and opinion mining", Proceedings of the 7th International Conference on Language Resources and Evaluation, pp.2200~2204 (2010)
- E. Riloff, "Learning extraction patterns for subjective expressions", Proceedings of the 2003Conference on Empirical Methods in Natural Language Processing, pp.105~112 (2003)
- C. Strapparava, "WordNet-Affect : An affective extension of WordNet", Proceedings of the 4th International Conference on Language Resources and Evaluation, pp.1083~1086 (2004)
- C. D. Manning, "Introduction to Information Retrieval" (2008), Cambridge University Press