文章基本信息

标题：An Approach for Big Data to Evolve the Auspicious Information from Cross-Domains
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作者：Preeti Arora ; Deepali Virmani ; P. S. Kulkarni 等
期刊名称：International Journal of Electrical and Computer Engineering
电子版ISSN：2088-8708
出版年度：2017
卷号：7
期号：2
页码：967-974
DOI：10.11591/ijece.v7i2.pp967-974
语种：English
出版社：Institute of Advanced Engineering and Science (IAES)
摘要：Sentiment analysis is the pre-eminent technology to extract the relevant information from the data domain. In this paper cross domain sentimental classification approach Cross_BOMEST is proposed. Proposed approach will extract † ve words using existing BOMEST technique, with the help of Ms Word Introp, Cross_BOMEST determines † ve words and replaces all its synonyms to escalate the polarity and blends two different domains and detects all the self-sufficient words. Proposed Algorithm is executed on Amazon datasets where two different domains are trained to analyze sentiments of the reviews of the other remaining domain. Proposed approach contributes propitious results in the cross domain analysis and accuracy of 92 % is obtained. Precision and Recall of BOMEST is improved by 16% and 7% respectively by the Cross_BOMEST.
其他摘要：Sentiment analysis is the pre-eminent technology to extract the relevant information from the data domain. In this paper cross domain sentimental classification approach Cross_BOMEST is proposed. Proposed approach will extract † ve words using existing BOMEST technique, with the help of Ms Word Introp, Cross_BOMEST determines † ve words and replaces all its synonyms to escalate the polarity and blends two different domains and detects all the self-sufficient words. Proposed Algorithm is executed on Amazon datasets where two different domains are trained to analyze sentiments of the reviews of the other remaining domain. Proposed approach contributes propitious results in the cross domain analysis and accuracy of 92 % is obtained. Precision and Recall of BOMEST is improved by 16% and 7% respectively by the Cross_BOMEST.
关键词：Sentimental Classification;Labelled Words;BOG;Feature Extraction