1M. Tech Program, Advanced Institute of Technology and Management, Maharishi Dayanand University, Rohtak, Haryana
2Head, Dept. of Computer Science and Engineering, Advanced Institute of Technology and Management, Maharishi Dayanand University, Rohtak, Haryana
Opinions, sentiments, evaluations, attitudes, and emotions are the subjects of study of sentiment analysis and opinion mining. The inception and rapid growth of the field coincide with those of the social media on the Web, e.g., reviews, forum discussions, blogs, micro blogs, Twitter, and social networks, because, we have a huge volume of opinionated data recorded in digital forms. Since early 2000, opinion mining has grown to be one of the most active research areas in natural language processing. It is also widely studied in data mining, Web mining, and text mining. In fact, it has spread from computer science to management sciences and social sciences due to its importance to business and society as a whole. In recent years, industrial activities surrounding opining mining have also thrived.
Opining mining is a type of natural language processing for tracking the mood of the public about a particular product or topic. Opining mining, which is also called Sentiment analysis, involves in building a system to collect and examine opinions about the product made in blog posts, comments, reviews or tweets. Opining mining can be useful in several ways. For example, in marketing it helps in judging the success of an ad campaign or new product launch, determine which versions of a product or service are popular and even identify which demographics like or dislike particular features. In our thesis we proposed a opining mining system of online mobile phone reviews using naïve bayes classification and maximum entropy model. The technique will perform Opining mining and give acquiescent result and will also compare the performance of naïve bayes classification and maximum entropy model.
Naïve Bayes, Maximum Entropy Model, Opinion Mining
