Twitter Sentiment Analysis

This project focuses on sentiment analysis of Twitter data using a dataset from Kaggle. It preprocesses the data and builds a classification model to determine whether a tweet has a positive, negative, or neutral sentiment.

Table of Contents

• Project Overview
• Installation
• Usage
• Technologies Used
• Dataset
• Results
• Contributing
• License

Project Overview

This project uses a dataset sourced from Kaggle to analyze the sentiment of tweets. The process includes:

• Installing necessary dependencies.
• Fetching and extracting the dataset.
• Preprocessing the text data by removing stop words and performing stemming.
• Building a classification model using Logistic Regression to classify tweets as positive, negative, or neutral.

Installation

Follow these steps to set up the project:

1. Install Kaggle Library: The project uses the Kaggle API to download the dataset:

   pip install kaggle

2. Configure Kaggle Credentials: Ensure you have a Kaggle API key (kaggle.json) configured:

   # Copy kaggle.json to the appropriate directory
   mkdir -p ~/.kaggle
   cp /path/to/kaggle.json ~/.kaggle/
   chmod 600 ~/.kaggle/kaggle.json

3. Download the Dataset: Use the Kaggle API to download the sentiment analysis dataset:

   kaggle datasets download -d kazanova/sentiment140

4. Extract the Dataset: Extract the dataset from the zip file:

   from zipfile import ZipFile
   dataset = '/content/sentiment140.zip'
   with ZipFile(dataset, 'r') as zip:
       zip.extractall()
       print("The dataset is extracted")

Usage

1. Preprocess the Data: The text data is cleaned and preprocessed, including the removal of stopwords and stemming using PorterStemmer from the NLTK library.

   Example of importing dependencies:

   import re
   import nltk
   from nltk.corpus import stopwords
   from nltk.stem.porter import PorterStemmer
   from sklearn.feature_extraction.text import TfidfVectorizer

2. Load the Dataset: Load the sentiment dataset into a Pandas DataFrame:

   twitter_data = pd.read_csv('/content/training.1600000.processed.noemoticon.csv', encoding='ISO-8859-1')

3. Train the Model: Split the data into training and testing sets, then use Logistic Regression to train the sentiment classifier:

   from sklearn.model_selection import train_test_split
   from sklearn.linear_model import LogisticRegression
   from sklearn.metrics import accuracy_score
   
   model = LogisticRegression()
   model.fit(X_train, y_train)

Technologies Used

• Python
• Libraries:
  • Numpy
  • Pandas
  • Scikit-learn
  • NLTK (Natural Language Toolkit)

Dataset

The dataset used for this project is the Sentiment140 dataset from Kaggle. It contains 1.6 million tweets labeled with sentiment (positive, negative, or neutral).

• Source: Sentiment140 Dataset

Results

The Logistic Regression model was trained on the dataset and evaluated using accuracy. Further results are available in the notebook.

Contributing

If you'd like to contribute, feel free to submit a pull request or raise an issue.

License

This project is licensed under the MIT License.