Twitter-Analysis

Overview

This project aims to perform sentiment analysis on Twitter data using natural language processing (NLP) techniques. By analyzing the sentiment of tweets, we can gain insights into public opinion, trends, and emotions surrounding various topics or events.

Features

1. Data Collection: Utilizes the Twitter API to collect tweets based on specific keywords, hashtags, or user handles.
2. Preprocessing: Cleans and preprocesses the raw tweet data by removing noise, such as special characters, URLs, and mentions.
3. Sentiment Analysis: Applies machine learning or lexicon-based techniques to determine the sentiment polarity (positive, negative, or neutral) of each tweet.
4. Visualization: Generates visualizations such as bar charts, pie charts, or word clouds to represent the distribution of sentiments or most frequent terms.

Technologies Used

1. Python
2. Tweepy (Twitter API library)
3. NLTK (Natural Language Toolkit) or SpaCy (NLP library)
4. Scikit-learn (Machine learning library)
5. Matplotlib and Seaborn (Data visualization libraries)
6. Flask or Dash (for dashboard creation)