Research Article | OPEN ACCESS
Effective Sentiment Analysis for Opinion Mining Using Artificial Bee Colony Optimization
T.M. Saravanan and A. Tamilarasi
Department of Computer Applications, Kongu Engineering College, Perundurai, Erode,
Tamil Nadu 638052, India
Research Journal of Applied Sciences, Engineering and Technology 2016 8:828-840
Received: April 17, 2015 | Accepted: January 8, 2016 | Published: April 15, 2016
Abstract
Opinions play important role in the process of knowledge discovery or information retrieval and can be considered as a sub discipline of Data Mining. The huge quantity of information on web platforms put together feasible for exercise as data sources, in applications based on opinion mining and classification. An effective sentiment analysis process proposes in this research for mining and classifying the opinions. The phases of the proposed research are: (1) Data Pre-processing Phase (2) Potential Feature Extraction Phase (3) Opinion Extraction and Mining Phase and (4) Opinion Classification Phase. Initially, the datasets from various web documents get preprocessed and gives as part-of-speech tagged text. An Improved High Adjective Count (IHAC) Algorithm employs on the Part-Of-Speech tagged text to extract the potential features. Improved High Adjective Count Algorithm effectively optimizes the scores of the nouns to extract the potential features. An Artificial Bee Colony (ABC) Algorithm works under the IHAC algorithm for providing opinion scores and also for giving ranks for every noun. Max Opinion Score Algorithm can be then helpful to extract the opinion words followed by the classification phase, in which, ID3 algorithm utilizes to classify the review into three kinds positive, negative and neutral based on the opinions. The implementation is carried out on Customer Review Datasets and Additional Review Datasets with the aid of JAVA platform and also the experimentation results are analyzed.
Keywords:
Artificial bee colony algorithm, ID3 algorithm, improved high adjective count algorithm, max opinion score algorithm, opinion mining,
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Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Copyright
The authors have no competing interests.
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