A Novel Feature Engineering Approach for Twitter-Based Text Sentiment Analysis - Nandy, Hiran; Sridhar, Rajeswari (Evolving Technologies for Computing, Communication and Smart World, 2020)
With the increasing availability of handheld devices and greater affordability of mobile data, social media has become an inseparable part of the daily life of most of the society. Free availability, diversity, and massiveness of this data have inspired many research work to use it for extracting various insights about a variety of subjects. Text sentiment analysis is the process of mining opinion polarity from a given document in natural language. Social media data is no exception to extract sentiments which could be used for a variety of tasks right from opinion mining to recommendations. In this work, Twitter has been chosen as the source of data corpus required due to the summarized content, ease of availability, and humongous reach among all classes of the society. This work uses traditional machine learning approaches for solving the problem of text sentiment analysis and proposes a novel feature engineering approach for merging the text-based and non-textual features of the dataset by including the predicted output lists also in the final feature set. The result analysis shows significant improvement in the performance of the machine learning model on the test dataset using the proposed approach.