Tweeter Sentiment Analysis using LSTM

Project Code :TCMAPY988

Objective

The Machine Learning objective of Twitter Sentiment Analysis using LSTM (Long Short-Term Memory) is to analyze and classify the sentiments of tweets into categories like positive, negative, or neutral. By leveraging LSTM's ability to remember sequential information, the analysis provides deeper insights into public opinion, enabling better decision-making in areas such as marketing, politics, or social trends.

Abstract

The classification of emotional states from poetry or formal text has received less attention by the experts of computational intelligence in recent times as compared to informal textual content like SMS, email, chat, and online user reviews. In this study, an emotional state classification system for text is proposed using the latest and cutting edge technology of Artificial Intelligence, called Deep Learning. For this purpose, an attention-based LSTM model along with GRU is implemented on the text corpus. The proposed approach classifies the text into different emotional states, like neutral, joy, fear, sadness and anger.

Keywords: Deep learning, emotion recognition, text, attention-based LSTM, formal text, emotional states

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.