Effective Sentiment Analysis for Opinion Mining Using Artificial Bee Colony Optimization

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.