文章基本信息

标题：Mining of Sentence Level Opinion Using Supervised Term Weighted Approach of Naïve Bayesian Algorithm
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作者：Trivedi Khushboo N ; Swati K. Vekariya ; Prof.Shailendra Mishra 等
期刊名称：International Journal of Computer Technology and Applications
电子版ISSN：2229-6093
出版年度：2012
卷号：3
期号：3
页码：987-991
出版社：Technopark Publications
摘要：Mining is used to help people to extract valuable information from large amount of data. As the addictive use of computers and 3G high speed internet have taken place in our day to day life, so there are so many user generated opinions on the web for the popular product. Now, from all those opinions , it is so difficult to know, how many opinions are negative, positive. It makes tough for them people to take conform decision about the purchasing of the product. And at the same time it is also difficult for the manufacturer to keep the track of the opinions and manage the opinions. For that, in this paper to help the people for making correct decision for the product, analysis and mining of the opinions are done at sentence level , because by this, we can come to know the views of so many people. This is done by the term counting based approach, in which total no of negative and positive words are count and then compared. If the dictionary is good then, it really gives good result. The algorithm used over here is naïve Bayesian algorithm, which is supervised. And for increasing the accuracy of this algorithm , it is changes in the terms of parameter which are passed to the algorithm
关键词：Sentence Level opinion mining; naïve Bayesian algorithm; Supervised Learning; Term Counting Based approach