Sentiment Analysis Based on Multiple Reviews by using Machine learning approaches

Sentiment is an attitude, thought, or judgment prompted by feeling. Sentiment Analysis can be defined as the process of analyzing online pieces of writing to determine the emotional tone they carry. With the vast growth of the social media content on the Internet in the past few years, people now express their opinion on almost anything in discussion. With respect to this, Bag–of–Words (BoW) is the most popular way to model text in statistical machine learning (ML) approaches. However, the performance of BoW sometimes remains unlimited due to some fundamental deficiencies in handling the polarity shift problem and other few challenges like quality of the opinions, hidden state representations, polarity categorization etc. To come across these challenges our focus will be on Dual Sentiment Analysis which processes the Sentiment with all the perspectives (positive, negative or neutral). This may lead towards the accurate prediction for final decision making based on the reviews given by the customers. The proposed work is being experimented on the Amazon Product reviews specifically the Mobile device reviews. This work aims at overcoming the limitation of existing system and improving the accuracy.