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Sentiment_Analysis.ipynb
sentiment_analysis_results.csv

**Sentiment Analysis of Tweets Using Transformers**: This project applies advanced natural language processing techniques to perform sentiment analysis on tweets. Leveraging the Hugging Face transformers library, we analyze the sentiment of tweets to identify underlying patterns and trends in public opinion. **Overview**: The goal of this project is to mine insights from social media data, particularly Twitter, to understand public sentiment on various topics. By utilizing a pre-trained sentiment analysis model from the transformers library, we classify tweets into positive, neutral, or negative sentiment categories. These insights aid strategic decision-making in marketing, customer service, and other domains. **Features**: Data Preprocessing: Clean and prepare tweet data for analysis. Sentiment Analysis: Use a pre-trained model from the transformers library to classify tweet sentiments. Trend Analysis: Analyze and visualize sentiment trends over time. Insight Generation: Provide actionable insights based on sentiment analysis. Installation Before running the project, ensure you have Python installed on your system. Then, install the required libraries using the following command: bash Copy code **pip install pandas matplotlib seaborn transformers tqdm nltk** Usage To run the sentiment analysis pipeline, execute the main script: **Data Format**: The input data should be a CSV file containing at least one column: tweet_full_text, which holds the text of the tweets. Additional columns such as tweet_created_at for timestamps can be included for trend analysis. **Visualizations**: This project generates two key visualizations: Sentiment Score Distribution: A histogram showing the distribution of sentiment scores across tweets. Daily Sentiment Trends: A line graph showing average daily sentiment scores over time.

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