Performance Evaluation of Domain-Specific Sentiment Dictionary Construction Methods for Opinion Mining
- Title
- Performance Evaluation of Domain-Specific Sentiment Dictionary Construction Methods for Opinion Mining
- Author
- 김종우
- Keywords
- Sentiment Analysis; Opinion Mining; Sentiment Dictionary; Sentiment Lexicon; SO-PMI
- Issue Date
- 2016-08
- Publisher
- Science and Engineering Research Support Society
- Citation
- International Journal of Database Theory and Application, v. 9, NO 8, Page. 257-268
- Abstract
- Sentiment dictionaries or lexicons are core elements for “bag-of-word” approaches of opinion mining or sentiment analysis. Rather than using general-purpose sentiment dictionaries, domain-specific sentiment lexicons can contribute to improve performance because they can reflect domain specific terms and meanings. This paper presents four domain-specific sentiment dictionary construction methods for opinion mining, and describes performance evaluation results using a practical data set. The comparison subjects of this research include SO-PMI (Semantic Orientation from Pointwise Mutual Information) and three term frequency-based methods with different term polarity measures. To evaluate the performance of four different methods, a movie review data set from a representative Internet movie community site, IMDb (Internet Movie Database) is collected using a web crawling program, and is analyzed using R programs. Based on training data set, domain specific sentiment dictionaries are constructed using four different methods, and are compared their performance of sentiment analysis. The experimental results show that domain-specific sentiment dictionaries are working better than general-purpose dictionaries except one genre, „animation‟. Also, term frequency-based approaches show better performance than SO-PMI.
- URI
- http://www.earticle.net/Article.aspx?sn=284297https://repository.hanyang.ac.kr/handle/20.500.11754/75051
- ISSN
- 2005-4270
- DOI
- 10.14257/ijdta.2016.9.8.24
- Appears in Collections:
- GRADUATE SCHOOL OF BUSINESS[S](경영전문대학원) > BUSINESS ADMINISTRATION(경영학과) > Articles
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