Main Article Content
With the advent of information exploration over the years, the most important thing to make the business decision is to consider the opinion of the peoples. Sometimes the opinions which are transformed to sentiments plays a vital role in the aforementioned areas. The opinion of the people is shared by the e-commerce, social media networks like twitter, facebook, blogs, forums, and etc., The opinion is categorized into positive, negative and neutral opinion. To extract the sentiments, opinions there are different approaches available in the literature such as support vector machine, Naïve Bayes, Neural Networks, N-gram, lexicon based approaches [12-15] etc. In this paper it is aimed at comparing different type of machine learning approaches such as supervised , unsupervised and semi supervised learning algorithms which are useful to extract the various opinions over the Net. It is further surveyed to understand various performance measures when the extracted opinions were transformed to sentiments.
How to Cite
Machine Learning, NLP tools, Natural Language Processing (NLP), Sentiment Analysis
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