Sentiment Analyzer to summarize user reviews of Software Products

Abstract

To find an appropriate software product for a requirement in the present software industry could be a challenging task. The main reason for this is that the rapid change of the software industry therefore numerous amounts of products are released with different qualities. To solve this issue, the proposed system would be an aspect based and web-based sentiment analyzer which suggests the most suitable product for the user by summarizing the user discussions of software products. The user will be able to acquire a summarized analysis of the software product required by categorizing according to their aspects such Usability and Stability: the key points considered when choosing a software product. To implement this system Natural language processing, Machine learning and Sentiment analysis were used in order to give a summary of the sentiment. Therefore, the accuracy of it is increased in order for the end user to purchase the best product with higher efficiency. The end product would be a angular-based web app so that its more convenient for the consumer.