Supervised approach of sentimentality extraction from bengali facebook status

Abstract

Sentiment is the only things that separate human and machine. To simulate the feelings for machines many researchers have been trying to create method and automated the process to extract opinion of particular news, product or life entity. Sentiment Analysis (SA) is a combination of opinions, emotions and subjectivity of a text. Currently SA is the most demanding task in Natural Language Processing. Social networking site like Facebook are mostly used in expressing the opinions about a particular entity of life. Newspaper published news about a particular event and user expressed their feedback in news comments. Online product feedback is increasing day by day. So reviews and opinions mining play a very important role in understanding people satisfactions. Such opinion mining has potential for knowledge discovery. The main target of SA is to find opinions from text extract sentiments from them and define their polarity, i.e positive or negative. In this domain most of the model was designed for English Language. This paper describes a novel approach using Naive Bayes classification model for Bengali Language. Here a supervised classification method is used with language rules for detecting sentiment for Bengali Facebook Status.