Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/123831
Title: AN OPTIMIZATION OF POLARITY CLASSIFICATION FOR SENTIMENT ANALYSIS USING METAHEURISTIC FITNESS FUNCTION
Researcher: JEEVANANDAM J
Guide(s): DR S KOTEESWARAN
Keywords: Opinion Mining, Sentiment Analysis, Polarity Classification, Genetic Algorithm, Multi Layer Perceptron, Neural Network
University: Vel Tech Dr. R R and Dr. S R Technical University
Completed Date: 5-12-2016
Abstract: Opinion Mining (OM) has a big impact in text mining applications regarding customer attitude identification, brand/product positioning, customer relationship management as well as market research. It is also referred as sentiment analysis and is a natural language processing method for tracking public moods regarding particular products or topics. OM constructs a model for collecting and examining product opinions in comments, tweets, blog posts or reviews. The aim is to rank opinions as very bad , bad , and average, good , very good and so on opinions are rated between 1 to 5. newlineIn the initial level of research, a weighted semantic feature expansion utilizing hyponym tree for features integration is proposed. To improve the efficacy of the classifiers, a decision tree based features selection is suggested. The proposed techniques were validated using the popular Internet Movie Database (IMDb) dataset for sentiment analysis and medical dataset, created from popular blogs. Multilayer Perceptron (MLP) Neural Network (NN) is used for classifying the selected features as positive or negative. As small networks have limited information processing power it might not provide good performance. In the proposed algorithm, the Back Propagation (BP) parameters namely learning rate and momentum are optimized using Genetic Algorithm (GA) and proposed hybrid GA (HGA) along with the structure of NN. The MLP-HGA method increased classification accuracy significantly.
Pagination: 
URI: http://hdl.handle.net/10603/123831
Appears in Departments:Department of Computer Science and Engineering

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01_title.pdfAttached File105.77 kBAdobe PDFView/Open
02_certificate.pdf181.44 kBAdobe PDFView/Open
03_declaration.pdf220.04 kBAdobe PDFView/Open
04_abstract.pdf134.81 kBAdobe PDFView/Open
05_acknowledgement.pdf139.89 kBAdobe PDFView/Open
06_contents.pdf113.59 kBAdobe PDFView/Open
07_list of tables.pdf84.18 kBAdobe PDFView/Open
08_list of figures.pdf159.1 kBAdobe PDFView/Open
09_list of abbreviations.pdf138.9 kBAdobe PDFView/Open
10_chapter 1.pdf383.14 kBAdobe PDFView/Open
11_chapter 2.pdf318.38 kBAdobe PDFView/Open
12_chapter 3.pdf401.16 kBAdobe PDFView/Open
13_chapter 4.pdf321.25 kBAdobe PDFView/Open
14_chapter 5.pdf386.21 kBAdobe PDFView/Open
15_chapter 6.pdf83.4 kBAdobe PDFView/Open
16_references.pdf218.28 kBAdobe PDFView/Open
17_publications.pdf126.34 kBAdobe PDFView/Open


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