Geographical Tweets Based Current Affairs Prediction Using Hybrid Features

Abstract

Twitter serves as a prominent and freely accessible social networking platform, enabling registered and authorized users to share their opinions and reviews through concise messages known as tweets. This research aims to conduct sentiment analysis on a Twitter dataset, seeking to predict and analyze current affairs based on user behaviour and opinions. The scope of current affairs encompasses a wide range of topics, including product reviews, political discourse, movie ratings, and more, expressed in both real-time streaming and offline data on the Twitter platform. During the feature extraction phase, a hybrid approach is employed, incorporating features such as acronyms, synonyms, and emoticons utilized by users in their tweets. Additionally, a hybrid dictionary is introduced, and instead of relying solely on unigrams and bigrams, a novel algorithm, n-gram, is implemented for more comprehensive analysis. This paper delves into various methodologies, specifically focusing on multiclass classification, and introduces a proposed system along with its noteworthy results. The research contributes to the understanding of user sentiments and behaviours on Twitter, offering valuable insights into diverse current affairs.