https://github.com/mastermindromii/sentiment-analysis-of-tweets-project
Welcome to the Twitter Sentiment Analysis project! 🐦📊 This repository contains code and resources to perform sentiment analysis on tweets.
https://github.com/mastermindromii/sentiment-analysis-of-tweets-project
Last synced: 3 months ago
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Welcome to the Twitter Sentiment Analysis project! 🐦📊 This repository contains code and resources to perform sentiment analysis on tweets.
- Host: GitHub
- URL: https://github.com/mastermindromii/sentiment-analysis-of-tweets-project
- Owner: MasterMindRomii
- Created: 2023-09-15T11:43:17.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-10-28T08:14:24.000Z (over 1 year ago)
- Last Synced: 2025-01-08T08:46:29.618Z (5 months ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 3.05 MB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Twitter Sentiment Analysis 🐦📊😃😡😐
Welcome to the Twitter Sentiment Analysis project! This repository contains code and resources to perform sentiment analysis on tweets. This README file provides an overview of the project, the tools used, and a brief example of the analysis.
## Table of Contents 📜
- [Project Overview](#project-overview)
- [Tools Used](#tools-used)
- [Getting Started](#getting-started)
- [Data Preprocessing](#data-preprocessing)
- [Exploratory Data Analysis](#exploratory-data-analysis)
- [Sentiment Analysis](#sentiment-analysis)
- [Evaluation](#evaluation)
- [Example](#example)
- [Contributing](#contributing)
## Project Overview 📋
Twitter sentiment analysis is the process of determining the sentiment or emotion expressed in a tweet, whether it's positive, negative, or neutral. This project focuses on analyzing tweets to gain insights into public opinion, customer feedback, and more.
## Tools Used 🛠️
In this project, we utilized several Python libraries for data analysis and natural language processing (NLP), including:
- `nltk` for text preprocessing
- `seaborn` and `matplotlib` for data visualization
- `scikit-learn` for machine learning
- `pandas` for data handling
## Getting Started 🚀
To start with the project, make sure you have the necessary Python libraries installed. You can do this using `pip` or `conda`. We recommend setting up a virtual environment to manage dependencies effectively.
## Data Preprocessing 📝
The first step in sentiment analysis is data preprocessing, which includes:
- Removing Twitter handles (e.g., @user)
- Eliminating punctuation, numbers, and special characters
- Removing short words
- Normalizing text, including stemming words
## Exploratory Data Analysis 📊
Before diving into sentiment analysis, we conduct exploratory data analysis (EDA) to understand the dataset's characteristics. EDA involves tasks like word cloud generation to visualize common terms.
## Sentiment Analysis 📈
The core of the project is sentiment analysis, which assigns a sentiment label (positive, negative, or neutral) to each tweet. Machine learning models, such as logistic regression, are trained to predict these labels based on the tweet text.
## Evaluation 📏
The project's success is evaluated using metrics like F1-score, accuracy, or precision and recall, depending on the problem's nature. Proper evaluation ensures the model's performance and its practicality.
## Example 📝
Here's an example of how to use this project for sentiment analysis:
```python
from sentiment_analysis import SentimentAnalyzer
# Create an instance of the SentimentAnalyzer
analyzer = SentimentAnalyzer()
# Analyze the sentiment of a tweet
tweet = "I love this product! It's amazing!"
sentiment = analyzer.analyze_sentiment(tweet)
print(f"The sentiment of the tweet is: {sentiment}")
Thank you for reading 🙏! Give me a ⭐ if you found it helpful. 😊