Opinion Analysis of Bi-Lingual Event Data from Social Networks

Abstract

Social media platforms have become the go-to medium for connecting people in this era of the internet. Twitter has emerged as a popular platform that allowsusers to share their views on current events and political organizations, providing a wealth of political information. The aim of this study is to utilize natural language processing techniques to analyze a dataset extracted from Twitter. This involves retrieving data from Twitter, performing sentiment analysis using deeplearning approaches, and creating a Python library that classifiesinput texts as either positive or negative. The training data used in this study included the Roman-Urdu language, comprising 89793 entries. Various classification models were employed to categorize emotions, with the ensemble technique ultimately used to determine the results. The LSTM classifier achieved an accuracy of 87%, while the Bert model performed the best with 90% accuracy.