Semantic sentiment analysis using context specific grammar

The increasing number of e-commerce and social networking sites are producing large amount of data pertaining to reviews of a product, restaurant etc. A keen observation reveals that the text data gathered from any social review site are specific to a context and are subjective in nature promoting varied perceptions of sentiments. The novel idea is to define context specific grammar as semantics for a particular domain. Our research aims to develop a scalable model where features obtained from matching semantic patterns are used to predict the sentiment polarity of movie reviews and also provide a sentiment score for each review. The proposed model is intended to be flexible so that it could be applied to any domain by redefining the semantics specific to that domain. There are many other models which give accuracies greater than 80% using various methods. A study suggests that 70% accurate program is as good as humans as they have varied perceptions of sentiment about a movie review as it is a subjective summary of a movie. Our model might give lesser accuracy but it uses a cognitive approach trying to catch these varied perceptions by learning from a combination of positive and negative grammars. Analyzing results from various experiments we find that Logistic Regression with SGD on Apache Spark performs better with accuracy of 64.12% while being highly scalable. High dependency on the grammars is a limitation of the model. Improvements can be done by defining different quality and quantity of grammars.