https://github.com/fahrettinsolak/ai-user-comment-and-sentiment-analysis-project

This project is a sentiment analysis of IMDb movie reviews using a Kaggle dataset and Natural Language Processing (NLP) techniques. It aims to classify reviews as either positive or negative using a Random Forest classifier.
https://github.com/fahrettinsolak/ai-user-comment-and-sentiment-analysis-project

deep-learning jupyter-notebook machine-learning nlp phyton

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This project is a sentiment analysis of IMDb movie reviews using a Kaggle dataset and Natural Language Processing (NLP) techniques. It aims to classify reviews as either positive or negative using a Random Forest classifier.

• Host: GitHub
• URL: https://github.com/fahrettinsolak/ai-user-comment-and-sentiment-analysis-project
• Owner: Fahrettinsolak
• Created: 2024-10-15T21:58:37.000Z (2 months ago)
• Default Branch: main
• Last Pushed: 2024-10-15T22:00:00.000Z (2 months ago)
• Last Synced: 2024-10-31T23:08:12.178Z (about 2 months ago)
• Topics: deep-learning, jupyter-notebook, machine-learning, nlp, phyton
• Language: Jupyter Notebook
• Homepage:
• Size: 12.7 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# IMDb Movie Review Sentiment Analysis Using NLP

This project is a sentiment analysis of IMDb movie reviews using a Kaggle dataset and Natural Language Processing (NLP) techniques. It aims to classify reviews as either positive or negative using a Random Forest classifier.

## Project Overview

The project involves:

1. Cleaning the data by removing HTML tags, punctuation, numbers, and stopwords.

2. Converting the cleaned text into a numeric format using Bag of Words (CountVectorizer).

3. Training a Random Forest classifier to predict the sentiment of movie reviews.

## Dataset

The dataset used for this project is labeled IMDb movie reviews from Kaggle. It contains 25,000 reviews with sentiments labeled as:

- 1 for positive reviews

- 0 for negative reviews

**Important:** After downloading the repository, please append the data from `NLPlabeledData2.tsv` to the end of `NLPlabeledData.tsv` before running the project. This step is essential for the proper functioning of the model.

## Installation & Usage

### 1. Clone the repository

```bash

git clone https://github.com/Fahrettinsolak/AI-User-Comment-And-Sentiment-Analysis-Project.git

```

### 2. Install dependencies

Make sure you have nltk, scikit-learn, pandas, matplotlib, BeautifulSoup4, and numpy installed. You can install them using the following command:

```bash

pip install -r requirements.txt

```

### 3. Download NLTK stopwords

Run the following Python command to download the necessary NLTK data:

```python

import nltk

nltk.download('stopwords')

```

### 4. Prepare the dataset

Ensure the dataset is in the correct format:

Append the contents of NLPlabeledData2.tsv to NLPlabeledData.tsv to include all necessary data.

### 5. Run the project

To train the model and test the sentiment analysis, run:

```bash

python sentiment_analysis.py

```

### 6. Example Usage

You can test the model with a custom comment:

```python

new_comment = "This movie was Terrible"

predict = model.predict(new_comment)

print("Sentiment:", "Positive" if predict == 1 else "Negative")

```