https://github.com/iiakshat/sentiment-analysis-using-nltk
This project is about performing Sentiment Analysis on the "IMDB 50K movie reviews" dataset using the Natural Language Toolkit (NLTK) library. By analyzing movie reviews and classifying them as positive or negative sentiments, you can gain valuable insights into audience reactions, user preferences, and overall sentiments towards movies.
https://github.com/iiakshat/sentiment-analysis-using-nltk
Last synced: about 1 month ago
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This project is about performing Sentiment Analysis on the "IMDB 50K movie reviews" dataset using the Natural Language Toolkit (NLTK) library. By analyzing movie reviews and classifying them as positive or negative sentiments, you can gain valuable insights into audience reactions, user preferences, and overall sentiments towards movies.
- Host: GitHub
- URL: https://github.com/iiakshat/sentiment-analysis-using-nltk
- Owner: iiakshat
- License: mit
- Created: 2023-07-28T07:57:45.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-07-28T08:09:05.000Z (over 1 year ago)
- Last Synced: 2025-01-20T19:25:29.263Z (3 months ago)
- Language: Jupyter Notebook
- Size: 75.4 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Sentiment-Analysis
📊 Sentiment Analysis using NLTK - GitHub Repository - By Akshat Sanghvi 📊
Welcome to the official GitHub repository for my Sentiment Analysis project using the Natural Language Toolkit (NLTK) library! 🚀
## Project Overview:
In this project, I performed sentiment analysis on the [IMDB 50,000 movie reviews](https://www.kaggle.com/datasets/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews) dataset from __Kaggle__. The main objective was to classify movie reviews as either positive or negative based on the emotions expressed in the text.
## Key Features:
🔍 __Data Exploration:__ I started by exploring the dataset to gain insights into its structure and content. Understanding the dataset was crucial for designing an effective sentiment analysis solution.
💡 __Text Preprocessing:__ To prepare the text data for analysis, I applied various text preprocessing techniques, including removing special characters, converting text to lowercase, and handling stop words.
🧮 __Tokenization and Feature Engineering:__ The NLTK library played a central role in tokenizing the text data and engineering meaningful features from the raw text.
🤖 __Sentiment Classification:__ I implemented machine learning models to classify the movie reviews as positive or negative sentiments. In my experiments, I found that ensemble techniques really work well for such projects.
📈 __Performance Evaluation:__ Model evaluation was a critical step in the project. I utilized various metrics, such as accuracy, precision, recall, and F1-score, to assess the model's performance and ensure its reliability. Also, in the end, I used seasborn library to visualise the confusion matrix. I also analyzed and visualised various important features affecting our classifier.
## How to Use:
To replicate the analysis and explore the results, follow these steps:
1. Clone this repository to your local machine using the command:
```
git clone - https://github.com/iiakshat/Sentiment-Analysis-using-NLTK
```
2. Install the required dependencies using `pip`:
```
pip install -r requirements.txt
```
3. Open the Jupyter Notebook `Notebook.ipynb` or `notebook.pdf` to access the complete code and documentation.
4. Execute each cell in the notebook to replicate the analysis step-by-step.
## Feedback and Contributions:
Feedback, suggestions, and contributions are always welcome! If you have ideas on improving the code or want to add more features to the analysis, feel free to open a pull request. Let's learn and grow together as a community of NLP enthusiasts! 🌱
## Join the Discussion:
If you're passionate about NLP, Sentiment Analysis, or anything related to data science, let's connect and discuss on [LinkedIn](https://www.linkedin.com/in/akshat-sanghvi-5140a7165/). I'd love to exchange ideas and collaborate on exciting projects! 🤝
Let's dive into the fascinating world of Sentiment Analysis and NLP together. Happy analyzing! 📊📈