Abstrait

An Efficient Surveillances of Products Based on Opinion Mining

Meenambigai B

Sentiment Analysis is a Natural Language Processing and Information Extraction task that aims to obtain writer‟s feelings expressed in positive or negative comments, questions and requests, by analysing a large numbers of documents. Generally speaking, sentiment analysis aims to determine the attitude of a speaker or a writer with respect to some topic or the overall totality of a document. In recent years, the exponential increase in the Internet usage and exchange of public opinion is the driving force behind Sentiment Analysis today. The Web is a huge repository of structured and unstructured data. The analysis of this data to extract latent public opinion and sentiment is a challenging task. Sentiment analysis is a technique to classify people‟s opinion in product reviews, blogs or social networks. It has different usages and has received much attention from researchers. In this study, we are interested in product feature based emoticons in sentiment analysis. In other words, we are more interested in identifying the opinion polarities (positive, neutral or negative) expressed on product features. This is termed as the product feature based sentiment analysis. Sentiment Analysis can be performed on both supervised and unsupervised dataset. Sentiment Analysis identifies the phrases and emoticons in a text that bears some sentiment. The sentiment can be objective facts or subjective opinions. It is necessary to distinguish between the two. It identifies the polarity and degree of the sentiment. Sentiments are classified as objective (facts), positive (denotes a state of happiness, bliss or satisfaction on part of the writer) or negative (denotes a state of sorrow, dejection or disappointment on part of the writer). The sentiments can further be given a score based on their degree of positivity, negativity or neutral. Whenever emoticons are used, their associated sentiment dominates the sentiment conveyed by text and forms a good proxy for intended sentiments.