Sentiment Analysis using Feature Based Support Vector Machine – A Proposed Method
Prakash P. Rokade1, Aruna Kumari D2

1Prakash P. Rokade, Ph.D, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation University, Vaddeswaram (Andhra Pradesh), India.
2Dr. Aruna Kumari, Professor, Department of CSE,VJIT, Hyderabad (Telangana) India.
Manuscript received on 19 October 2019 | Revised Manuscript received on 25 October 2019 | Manuscript Published on 02 November 2019 | PP: 3671-3676 | Volume-8 Issue-2S11 September 2019 | Retrieval Number: B14630982S1119/2019©BEIESP | DOI: 10.35940/ijrte.B1463.0982S1119
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Abstract: Business decisions for any service or product depend on sentiments by the people. The mood of people towards any event, service and product are expressed in sentiments. The text sentiment contains different linguistic features of sentence. A sentiment sentence also contains other features which are playing a vital role in deciding the polarity of sentiments.The features like duplication of sentiment, unknown emotics may change the polarity of sentiment.If features selection is proper one can extract better sentiments for decision making. A directed preprocessing will feed filtered input to any machine learning approach. Support vector machine proved as a good tool of machine learning for better sentiment analysis.Better use of parts os speech (POS) folled by guided preprocessing and evaluation will provide less errorus polarity of sentiments
Keywords: Feature Weighting, N Gram Model, Parts of Speech, Sentimentanalysis, Support Vector Machine.
Scope of the Article: Machine Learning