An Enhanced Technique for Analyzing Sentiments of Public Reviews – I
Chintan Panjwani1, Rashmi Thakur2
1Chintan Panjwani, M.E Computer Engineering Student, Thakur College of Engineering & Technology, Mumbai.
2Mrs. Rashmi Thakur, Assistant Professor, Thakur College of Engineering & Technology, Mumbai.
Manuscript received on September 15, 2019. | Revised Manuscript Received on September 20, 2019. | Manuscript published on September 20, 2019. | PP: 1-6 | Volume-5 Issue-6, September 2019. | Retrieval Number: D0926055419/2019©BEIESP | DOI: 10.35940/ijies.D0926.095619
Open Access | Ethics and Policies | Cite
© The Authors. Published By: Blue Eyes Intelligence Engineering & Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Sentiment analysis is the process of extracting the opinion expressed in a piece of text to determine the writer’s attitude towards a topic, product or any service in general and classify it into classes such as positive, negative or neutral. Bag of Words is the traditional approach for text representation in Sentiment Analysis where text is represented as bag of its words. This approach represents the text by breaking the sentence into words disregarding other semantic information. A problem that occurs due to this representation is Polarity Shift problem. To address polarity shift problem a dual sentiment analysis (DSA) system is created. It looks at the reviews from both the sides i.e. positive and negative. The existing work on dual sentiment analysis includes techniques where dual training and dual prediction is performed. The proposed system is to enhance the classification performance of the existing system by applying different classifiers apart from those used in existing system to obtain better results. After classification of reviews into appropriate classes, various graphs are plotted based on different parameters to validate the results and determine the best classifier from the applied classifiers
Keywords: Bag of Words, Enhanced Dual Sentiment Analysis, Polarity Shift problem, Sentiment Analysis, Support Vector Machine