An Enhanced Lexical Resource for Text Mining and Sentiment Investigation

Alam, Aftab and Irfan, Mohammed (2015) An Enhanced Lexical Resource for Text Mining and Sentiment Investigation. International Journal of Computer and Information Technology, 4 (2). pp. 479-483. ISSN 2279 – 0764

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In modern era Text Mining and Sentiment investigation is a growing research area, covering over several disciplines such as data mining, text mining, etc. Text mining is a vital art of extracting the texts from the huge set of text set or reviews. Sentiment investigation is a category of natural language processing for tracking the mood of the public about a specific product or theme. The present works of text mining used Sentiwordnet as a lexical resource. The major drawback of this present Sentiwordnet is non-determination of total count, such as it doesn’t offer the specific number of +1, -1 and 0 total words. These data and facts are necessary because without these details of total count if further data mining techniques are applied, it may give erroneous results. To facilitate the text mining task, this work focus on design of Developed Sentiwordnet so that it can produce the count of total words by distinguishing them into +1, -1 and 0 words. Experiments are conducted on standard movie review and product review datasets. These works also make use of Stanford POS tagger for labeling the dataset. The calculated words can be used to enhance the results comparatively better.

Item Type: Article
Subjects: Computer
Computer Sciences
Computer Sciences and Information Systems
Divisions: College of Computer Sciences > Computer Sciences
Depositing User: Mr Mohammed Irfan
Date Deposited: 27 Apr 2017 09:12
Last Modified: 27 Apr 2017 09:12

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