Twitter-Sentiment-Analysis

This project focuses on Twitter sentiment analysis using machine learning and NLP techniques. It involves preprocessing tweets, converting text data into numerical form using TF-IDF Vectorizer and training Logistic Regression to classify tweets as positive or negative. The trained model is saved for predictions.

Analyze and visualize sentiment patterns in social media data to understand public opinion and attitudes towards specific topics or brands.

About Dataset

Twitter Sentiment Analysis Dataset

Overview

This is an entity-level sentiment analysis dataset of twitter. Given a message and an entity, the task is to judge the sentiment of the message about the entity. There are three classes in this dataset: Positive, Negative and Neutral. We regard messages that are not relevant to the entity (i.e. Irrelevant) as Neutral.

In conclusion, the sentiment analysis provides valuable insights into the prevailing attitudes and opinions within the Twitter community regarding various topics. While negative sentiments appear to be more common overall, there is a diverse range of sentiments expressed across different topics.