Analyzing Stakeholders Response to Indian Governments EV Policy Through a Text Mining Approach The critical step for developing countries is policy formationreforms. The process of policy formation is focused on enhancing the quality of its citizenxe2x80x9cs life or society and transforming the economy of the country. There will always be a positivenegative impact on the industriescompanies within the policy implementation domain. Such conditions lead firms realizing the business advancement in planned policy initiates to shape or support it by helping the government. Contrary, firms sensing adverse effect on them due to proposed new policy, tries to resist or delay the deployment of policy. This article presents a case of an Indian Governmentxe2x80x9cs proposed xe2x80x98Electric Vehicle (EV) Policyxe2x80x99 and the reactions received to it from various stakeholders viz. firms under policy domain, consumers and different associations. The feedback from different stakeholders is examined in this study to determine how the transition took place in proposed new policy using Text Mining approach. The analysis consists of reactions of stakeholders to the new policy in terms of Shape, support, oppose and delay, and Governmentxe2x80x9cs change in its stand due to the industryxe2x80x9ds reaction.

ExtractionBased Text Summarization and Sentiment Analysis of Online Reviews Using Hybrid Classification Method The field of sentiment mining and text summarization has evoked the interest of many scientists and researchers over the last few years, as the textual data has become useful for many realworld applications and challenges. Sentiment Analysis and Opinion Mining is the most popular field for analyzing and discovering insights from text data from various sources, such as Facebook, Twitter and Amazon, Zomato, etc. It involves a computational study of an individualxe2x80x99s behavior in terms of buying interest and then extracting his opinions on the business entity of the company. This entity can be viewed as an event, individual, blog post or product experience. Scholars in the fields of natural language processing, data mining, machine learning and others have tested a variety of methods for automating sentiment analysis. These reviews are increasing on a daily basis, as a result of which the summarization of the reviews plays a role where the text is summarized as needed, which provides useful information from a large number of reviews. It is very difficult for a human being to extract and interpret useful data from a very large file. In the text analysis, the value of sentences is decided on the basis of the linguistic characteristics of sentences. This paper provides a comprehensive review of current and past work on sentiment analysis and text description. In this research work, a new hybrid classification system is proposed based on coupling classification methods using arcing classifiers and their quality is evaluated within terms of accuracy. The Classifier Collection was constructed using Naive Bayes (NB), Support Vector Machine (SVM) and Genetic Algorithm (GA). The proposed work consists of a comparative study of the efficacy of the ensemble technique for sentiment classification. The feasibility and benefits of the proposed approaches are demonstrated by a restaurant review that is widely used in the field of sentiment classification. A wide range of comparative studies is performed and, ultimately, some indepth analysis is addressed and conclusions are drawn on the efficacy of the ensemble technique for sentiment classification