This project provides an intelligent, mood-aware content discovery tool that analyzes Twitter-like text input and recommends movies and books based on detected sentiment. The system utilizes a RoBERTa-based sentiment analyzer and a genre mapping engine to connect emotional tone with personalized media suggestions.

• RoBERTa Twitter Sentiment Analyzer

  • Detects Positive, Neutral, and Negative sentiments
  • Outputs sentiment probabilities and dominant sentiment

• Genre Mapping System

  • Maps sentiment → genres
  • E.g., Positive → Comedy, Romance; Negative → Horror, Psychological

• Media Recommendation Engine

  • Recommends mock book/movie data based on sentiment
  • Includes title, author/director, genre, year, and description

• Interactive Streamlit Interface

  • Analyze a single text input and get instant media suggestions
  • Upload CSV for batch analysis and visualize sentiment insights
  • Explore genre mapping logic

📁 project-root/
│
├── app.py              # Streamlit app
├── sentiment.py        # Sentiment analysis and recommendation logic
├── requirements.txt    # Python dependencies
└── README.md           # Project overview

1. Clone the repository:

git clone https://github.com/mvharsh/Movie-and-Book-Recommendations.git
cd Movie-and-Book-Recommendations

2. **Install dependencies**:

```bash
pip install -r requirements.txt

3. Run the application:

streamlit run app.py

• Uses cardiffnlp/twitter-roberta-base-sentiment
• Preprocesses input tweets for user mentions and links
• Outputs probabilities for Positive, Neutral, and Negative

• Sentiments are associated with emotional genres:
  • Positive: Comedy, Romance, Family, Animation...
  • Neutral: Sci-Fi, Documentary, History...
  • Negative: Horror, Psychological, Thriller...

• Mock APIs simulate data from TMDb and OpenLibrary
• Recommendations based on:
  • Dominant sentiment
  • Weighted sentiment profile

• Sentiment Probability Bar Charts
• Pie Charts for batch sentiment distribution
• Word Cloud per sentiment
• Stacked Genre Frequency Charts

id,text
1,"I just had an amazing coffee and I feel great!"
2,"The weather is so gloomy, it's making me sad."
3,"Watched a really thought-provoking documentary today."

https://www.youtube.com/watch?v=zh6AuxX8JVQ