Sentiment Analysis Project

This README provides step-by-step instructions for running the code snippets in a Jupyter Notebook for the sentiment analysis project.

Prerequisites

Before proceeding, ensure that you have the following installed on your system:

• Python (Python 3.x recommended)
• Jupyter Notebook to run the code interactively.
• The necessary libraries and dependencies mentioned in the project, which can be installed using pip as described in the project README.

Getting Started

1. Clone or Download the Project: Clone this project repository to your local machine using Git or download the project as a ZIP file and extract it to a directory of your choice.

2. Navigate to the Project Directory: Open your terminal or command prompt, and change your current working directory to the project directory:

   cd path/to/project/directory

3. Launch Jupyter Notebook: Start Jupyter Notebook by running the following command in your terminal:

   jupyter notebook

   This will open a new browser window displaying the Jupyter Notebook interface.

4. Open the Jupyter Notebook File: In the Jupyter Notebook interface, navigate to the project directory and open the Jupyter Notebook file containing the code snippets (e.g., SentimentAnalysisProject.ipynb).

5. Running Code Cells: Within the Jupyter Notebook, you can run code cells individually. To execute a code cell, select it and press Shift + Enter or click the "Run" button in the Jupyter Notebook toolbar.

Running the Code Snippets

Follow the order of code cells in the Jupyter Notebook to execute the project code snippets:

1. Data Loading: Run the code cell responsible for loading the dataset. Make sure to specify the correct file path to your dataset.

2. Data Preprocessing: Execute the code cells for data preprocessing to clean and prepare the text data.

3. Sentiment Analysis: Run the sentiment analysis code cells to perform sentiment analysis using TextBlob and machine learning-based methods.

4. Exploratory Data Analysis (EDA): Execute the code cells for EDA to explore the dataset and gain insights.

5. Data Visualization: Run the data visualization code cells to create charts and plots for visualizing sentiment distributions and trends.

6. Word Embeddings: Execute the code for training Word2Vec word embeddings and finding similar words.

7. Additional Analyses: Explore the code cells for additional data analysis tasks, such as analyzing correlations and visualizing tweet length and mentions.

8. Conclusion: Review the project's findings and insights generated by the analyses.

9. Generate Report: If necessary, customize and run code cells to prepare a comprehensive report summarizing the project's results and insights.

Customization

You may need to customize the code and analyses to fit your specific dataset and project objectives. Make sure to adapt the code, data file paths, and configurations as needed.