SocialUnrestPrediction is a sentiment analysis project focused on understanding intricate emotions expressed within discussions centered around critical societal movements. The project explores social discourse related to movements like Black Lives Matter, 'Stop Asian Hate,' and 'Immigration,' aiming to discern and classify emotions beyond conventional sentiments.

Leveraging Python's NLTK and Gensim libraries, SocialUnrestPrediction goes beyond standard sentiment analysis, exploring a diverse spectrum of emotions prevalent in online discussions. This project aims to uncover nuanced emotions such as fear, sadness, joy, and more, providing a deeper understanding of sentiments expressed in these critical societal movements.

The project employs machine learning techniques, including LSTM networks and GloVe word embeddings, to capture the complexity and depth of emotions embedded in textual data. SocialUnrestPrediction meticulously processes textual information from platforms like Reddit, uncovering emotions that go beyond basic categorizations.

• Analyzes emotions in discussions related to societal movements
• Delves into nuanced emotions like fear, joy, and sadness
• Utilizes NLTK, Gensim, and machine learning for sentiment analysis
• Achieves a robust accuracy of 94.95% in discerning complex emotions

1. Clone the repository
2. Install necessary Python libraries
3. Execute the main project file

The project uses a curated dataset sourced from Reddit discussions related to critical societal movements and an another dataset was picked up form the kaggle website to train the model. The dataset includes various threads and posts expressing diverse sentiments and emotions.

Contributions and feedback are welcome! Feel free to fork this repository, propose changes, or share your insights to enhance SocialUnrestPrediction further.