GenNext—An Extractive Graph-Based Text Summarization Model for User Reviews

User opinions are considered as the core of any business as it helps in reviewing the product or the service. It is the voice of the customer and every business strives to get full benefit from it. Relatively, this research work is based on analysis of the user reviews to provide a better approach to effectively understand the core of the numerous customer reviews present over the Web. It is almost inhumane job to search and read all these user reviews manually, and hence, the concepts of text summarization are applied with the sole aim to derive the meaningful abstract of the entire review for quicker understanding of its content. This research article peculiarly focuses on preparing a genre independent review summary model that can be used on any existing systems. GenNext is a unique model which provides extractive text summary for multi-genre data and even for user review of as less as one sentence is developed with graphical method of extractive text summarization. This model has been proven significantly accurate in providing effective summaries as compared to the existing summarization models in the market, with the help of sentiment analysis and polarity classification through natural language processing techniques. The summaries are also assigned related emoticons based on their polarity scores for animated representation.