Fuzzy Lexicon-Based Approach for Sentiment Analysis of Blog and Microblog Text
| Autor: | Vaishali Kalra, Srishti Sharma |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Computer science
business.industry Microblogging InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL Sentiment analysis Contrast (statistics) computer.software_genre Lexicon ComputingMethodologies_ARTIFICIALINTELLIGENCE Fuzzy logic Set (abstract data type) Text mining Social media Artificial intelligence InformationSystems_MISCELLANEOUS Computational linguistics business computer Natural language processing |
| Zdroj: | Research in Intelligent and Computing in Engineering ISBN: 9789811575266 |
| DOI: | 10.1007/978-981-15-7527-3_66 |
| Popis: | Sentiment analysis refers to determining the polarity of a document through natural language processing, text analytics and computational linguistics. With the continuous explosion of opinionated online text from blog Web sites, microblogs and product review Web sites, investigations in sentiment analysis are growing manifold. However, most of the sentiment analysis approaches are either lexicon-based or machine learning-based or a hybrid of these two. Very few researchers have explored the use of fuzzy logic in sentiment analysis. In this work, we propose a fuzzy lexicon-based approach for unsupervised sentiment analysis involving multiple lexicons and datasets. The crux of this work is the set of fuzzy rules proposed to assess the sentiment of blogs and microblogs. Experimental results verify the suitability of this approach for SA. We also contrast our work with the existing fuzzy sentiment analysis approaches to establish the superiority of the proposed system. |
| Databáze: | OpenAIRE |
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