- The main purpose of our analysis to highlight the biggest event in the middle east the last 6 months, which is isreal palestine war in addition to produce meaningful outputs of opinions on the IsrealPalestine subreddit posts and comments. The code last running time was 2024/1/28.
- This project conducts comprehensive sentiment analysis and exploratory data analysis (EDA) on discussions within the IsraelPalestine subreddit, focusing on the sentiment polarity of posts and comments, temporal trends, controversial topics, and prevalent themes through topic modeling. Leveraging machine learning models and natural language processing techniques, sentiment analysis uncovers prevailing sentiment trends, while EDA unveils hidden patterns and relationships within the dataset. Additionally, the project estimates user locations and identifies controversial topics, providing valuable insights into community dynamics. Furthermore, topic modeling techniques such as Latent Dirichlet Allocation (LDA) extract prevalent themes driving discussions within the subreddit.
- Data Exploration and Cleaning: Thorough exploration and cleaning of the dataset to ensure data quality.
- Sentiment Analysis: Classification of sentiments as positive, negative, or neutral in both posts and comments.
- Posts Sentiment Analysis: Analysis of sentiment trends within posts shared on the subreddit.
- Comments Sentiment Analysis: Granular insights into sentiment polarity at the comment level.
- EDA for the Dataset: Uncovering hidden patterns, relationships, and anomalies through visualization techniques and statistical summaries.
- Descriptive Statistics: Providing summarized information about dataset characteristics.
- 🍉 Analysis: In-depth exploration of unique patterns, trends, and observations within the dataset.
- Geopolitical Determination: Estimation of likely geographic locations of subreddit participants.
- Controversiality Analysis: Identification of contentious topics and debates within the subreddit.
- Topic Modeling: Identification of prevalent themes driving the discourse within the subreddit.
- Clone the Repository: Clone this repository to your local machine using the git clone command.
- Install Dependencies: Ensure Python is installed and install necessary libraries using pip install -r requirements.txt.
- Explore the Notebooks: Navigate through Jupyter notebooks provided to delve into data analysis and glean insights derived from the dataset.
- Feel free to contribute to this project by submitting pull requests, opening issues to suggest improvements, or reporting bugs. Your contributions are invaluable in enhancing the comprehensiveness and robustness of this analysis.