A Proposed Method for Predicting Indian Loksabha Election Using Machine Learning in Social Media - Vishnoi, Deeksha; Patel, Brajesh (International Journal of Computer Sciences and Engineering, 2025)
Sentiment analysis is the computational study of opinions, sentiments, evaluations, attitudes, views and emotions expressed in text. It refers to a classification problem where the main focus is to predict the polarity of words and then classify them into positive or negative sentiment. Sentiment analysis over Twitter offers people a fast and effective way to measure the public’s feelings towards their party and politicians. The primary issues in previous sentiment analysis techniques are classification accuracy, as they incorrectly classify most of the tweets with the biasing towards the training data. Sentiment analysis is considered to be a category of machine learning and natural language processing. It is used to extricate, recognize, or portray opinions from different content structures, including news, audits and articles and categorizes them as positive, neutral and negative. It is difficult to predict election results from tweets in social media using different platforms. We performed data (text) mining on thousands of tweets collected over a period of a month that referenced five national political parties in India, during the campaigning period for general state elections in 2018. We made use of both supervised and unsupervised approaches. We utilized Dictionary Based, Logistic Regression algorithm as the main algorithm to build our classifier and classified the test data as positive, negative and neutral. We identified the sentiment of Twitter users towards each of the considered Indian political parties. The result of the analysis was for the BJP (Bhartiya Janta Party). Proposed algorithm predicted a chance that the BJP would win more elections in the general election. Therefore, here we adopt a lexicon based sentiment analysis method, which will exploit the sense definitions, as semantic indicators of sentiment. Our method also uses a negation handling as a pre-processing step in order to achieve high accuracy