Whenever a decision is made, it depends on the opinions that one take. So, opinions are important. Emoticons express the opinions very well but from the text, it becomes difficult to find out the sentiments and opinions of one. Sentiment analysis is the process of detecting one’s opinions regarding a person, product, text. This paper covers the challenges and basic work flow of sentiment analysis.
Index Terms — Classification, Naïve Bayes, Opinions, Sentiment analysis.
1. INTRODUCTION
Emotion is a subjective, conscious experience characterized mainly by psycho-physiological expressions, biological reactions, and mental states. Emotion is often associated and considered commonly significant with mood, nature, personality, disposition, and motivation. Emotion is a positive or negative experience that is associated with a particular pattern of physiological activity. Humans carry lot of emotions like happiness, sadness, angry, disgust, surprise, fear, panic, scared etc. identifying these emotions are very easy in face to face communication compare to written communication. But now a day’s use of social media has increased rapidly and the huge amount of textual data became available on web, mining and managing this vast data has become a crucial task.
Sentiment analysis is nothing but
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The main aim of this analysis is to explore highly efficient machine learning technique. In future, Opinion Mining can be carried out on a set of reviews and set of discovered feature expressions extracted from reviews. The state-of-art for current methods, useful for producing better summary based on feature based opinions as positive, negative or neutral is the Expectation Maximization algorithm based on Naïve Bayesian is the most efficient method. In the later work, we would be focusing on eliminating the challenges faced in sentiment analysis and extracting the sentiments from the