Research Paper | Computer Science & Engineering | India | Volume 4 Issue 8, August 2015
Weighted Sentiment Analysis Using Artificial Bee Colony Algorithm
Ruby Dhurve | Megha Seth 
Abstract: Data mining is the process of extracting interesting and useful data from different perspective and summarizing into useful information. Sentiment analysis is an application of NLP, data mining and text mining to identify sentiments or mood of the public about particular topic or products or customer reviews. This paper proposes to improve the methods for classification of review and also detect the polarity of reviews using machine learning approach. Objective of the research paper is to select the best features selection methods for sentiments analysis. Bog of noun, bog of words, stop word removal, stemmer are used for feature selection. ABC algorithm is used for classification of text in three classes negative, positive and neutral. The main aim of the thesis is to compute the result of SVM and ABC classifier. In result nature inspired ABC classifier BON give the better results than the BOW, SVM with both BON and BOW.
Keywords: Sentiment Analysis, Feature selection, BOW Bag of Words, BON Bag of Nouns, Parser, Artificial Bee Colony Algorithm, Support Vector Machine
Edition: Volume 4 Issue 8, August 2015,
Pages: 1717 - 1722
How to Cite this Article?
Ruby Dhurve, Megha Seth, "Weighted Sentiment Analysis Using Artificial Bee Colony Algorithm", International Journal of Science and Research (IJSR), Volume 4 Issue 8, August 2015, pp. 1717-1722, https://www.ijsr.net/get_abstract.php?paper_id=SUB157637
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