Opinion Mining from Online User Reviews Using Fuzzy Linguistic Hedges
Nowadays, there are several websites that allow customers to buy and post reviews of purchased products, which results in incremental accumulation of a lot of reviews written in natural language. Moreover, conversance with E-commerce and social media has raised the level of sophistication of online...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2014/735942 |
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| _version_ | 1832560325717655552 |
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| author | Mita K. Dalal Mukesh A. Zaveri |
| author_facet | Mita K. Dalal Mukesh A. Zaveri |
| author_sort | Mita K. Dalal |
| collection | DOAJ |
| description | Nowadays, there are several websites that allow customers to buy and post reviews of purchased products, which results in incremental accumulation of a lot of reviews written in natural language. Moreover, conversance with E-commerce and social media has raised the level of sophistication of online shoppers and it is common practice for them to compare competing brands of products before making a purchase. Prevailing factors such as availability of online reviews and raised end-user expectations have motivated the development of opinion mining systems that can automatically classify and summarize users’ reviews. This paper proposes an opinion mining system that can be used for both binary and fine-grained sentiment classifications of user reviews. Feature-based sentiment classification is a multistep process that involves preprocessing to remove noise, extraction of features and corresponding descriptors, and tagging their polarity. The proposed technique extends the feature-based classification approach to incorporate the effect of various linguistic hedges by using fuzzy functions to emulate the effect of modifiers, concentrators, and dilators. Empirical studies indicate that the proposed system can perform reliable sentiment classification at various levels of granularity with high average accuracy of 89% for binary classification and 86% for fine-grained classification. |
| format | Article |
| id | doaj-art-8703cc23e5644c00af51ad0f706f5927 |
| institution | Kabale University |
| issn | 1687-9724 1687-9732 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-8703cc23e5644c00af51ad0f706f59272025-02-03T01:27:49ZengWileyApplied Computational Intelligence and Soft Computing1687-97241687-97322014-01-01201410.1155/2014/735942735942Opinion Mining from Online User Reviews Using Fuzzy Linguistic HedgesMita K. Dalal0Mukesh A. Zaveri1Information Technology Department, Sarvajanik College of Engineering & Technology, Surat 395001, IndiaComputer Engineering Department, S. V. National Institute of Technology, Surat 395007, IndiaNowadays, there are several websites that allow customers to buy and post reviews of purchased products, which results in incremental accumulation of a lot of reviews written in natural language. Moreover, conversance with E-commerce and social media has raised the level of sophistication of online shoppers and it is common practice for them to compare competing brands of products before making a purchase. Prevailing factors such as availability of online reviews and raised end-user expectations have motivated the development of opinion mining systems that can automatically classify and summarize users’ reviews. This paper proposes an opinion mining system that can be used for both binary and fine-grained sentiment classifications of user reviews. Feature-based sentiment classification is a multistep process that involves preprocessing to remove noise, extraction of features and corresponding descriptors, and tagging their polarity. The proposed technique extends the feature-based classification approach to incorporate the effect of various linguistic hedges by using fuzzy functions to emulate the effect of modifiers, concentrators, and dilators. Empirical studies indicate that the proposed system can perform reliable sentiment classification at various levels of granularity with high average accuracy of 89% for binary classification and 86% for fine-grained classification.http://dx.doi.org/10.1155/2014/735942 |
| spellingShingle | Mita K. Dalal Mukesh A. Zaveri Opinion Mining from Online User Reviews Using Fuzzy Linguistic Hedges Applied Computational Intelligence and Soft Computing |
| title | Opinion Mining from Online User Reviews Using Fuzzy Linguistic Hedges |
| title_full | Opinion Mining from Online User Reviews Using Fuzzy Linguistic Hedges |
| title_fullStr | Opinion Mining from Online User Reviews Using Fuzzy Linguistic Hedges |
| title_full_unstemmed | Opinion Mining from Online User Reviews Using Fuzzy Linguistic Hedges |
| title_short | Opinion Mining from Online User Reviews Using Fuzzy Linguistic Hedges |
| title_sort | opinion mining from online user reviews using fuzzy linguistic hedges |
| url | http://dx.doi.org/10.1155/2014/735942 |
| work_keys_str_mv | AT mitakdalal opinionminingfromonlineuserreviewsusingfuzzylinguistichedges AT mukeshazaveri opinionminingfromonlineuserreviewsusingfuzzylinguistichedges |