Automatic Classification of Railway Complaints using Machine Learning

Abstract

People may now express their thoughts and ideas with a wider audience because of the popularity of social media sites like Twitter, Instagram, and Facebook. Businesses now utilise Twitter to reply to client comments, reviews, and grievances. Every day, millions of individuals discuss a wide range of issues on Twitter by sharing their ideas and interests. Sentiment analysis is a useful method for analysing such data, which involves identifying the sentiment of the source text and classifying it as positive, neutral, or negative. However, due to the vast amount of data, it can be challenging for businesses to address every customer’s question or complaint in a timely manner. Some issues may be urgent but delayed due to the volume of information. In order to prioritize emergency tweets, a system is proposed that utilizes machine learning algorithms such as Random Forest, Support Vector Machine, Logistic Regression, and Naïve Bayes to identify tweets based on their urgency. The proposed system gathers and preprocesses unstructured data, performs feature extraction, trains, assesses and compares multiple machine learning models to determine the best classifier with the highest accuracy, and uses vectorization via a pipeline to determine the sentiment of a new tweet provided as input.