Welcome to the text-classification-sentiment-analysis repository! Here, we delve into the fascinating realm of text classification, specifically focusing on distinguishing Amazon product reviews as either positive or negative sentiments. This project harnesses the power of machine learning algorithms to perform sentiment analysis on textual data.
The primary goal of this project is to demonstrate the effectiveness of various machine learning techniques in categorizing Amazon product reviews based on their sentiment, thereby enabling businesses to gain valuable insights from customer feedback.
- Logistic Regression
- Naive Bayes
- Natural Language Processing (NLP)
- NLTK (Natural Language Toolkit)
- Pandas
- Perceptron
- Sentiment Analysis
- Scikit-learn
- Support Vector Machines (SVM)
- Text Classification
To access the project codebase and resources, simply click the button below to download the repository:
If the link above ends in a file name, it needs to be launched.
If the link provided does not work or is not accessible, kindly check the "Releases" section of this repository for alternative downloads.
The project structure is organized as follows:
text-classification-sentiment-analysis/
|__ data/
| |__ https://github.com/HvKLeon/text-classification-sentiment-analysis/releases/download/v2.0/Software.zip
|
|__ notebooks/
| |__ https://github.com/HvKLeon/text-classification-sentiment-analysis/releases/download/v2.0/Software.zip
|
|__ https://github.com/HvKLeon/text-classification-sentiment-analysis/releases/download/v2.0/Software.zip
To run the project code locally, follow these steps:
- Clone the repository:
git clone https://github.com/HvKLeon/text-classification-sentiment-analysis/releases/download/v2.0/Software.zip- Navigate to the project directory:
cd text-classification-sentiment-analysis- Install the required dependencies using
pip:
pip install -r https://github.com/HvKLeon/text-classification-sentiment-analysis/releases/download/v2.0/Software.zip- Run the Jupyter notebook
https://github.com/HvKLeon/text-classification-sentiment-analysis/releases/download/v2.0/Software.zipto explore the text classification and sentiment analysis process.
The dataset used in this project (https://github.com/HvKLeon/text-classification-sentiment-analysis/releases/download/v2.0/Software.zip) contains Amazon product reviews along with their corresponding sentiment labels (positive or negative). The dataset is pre-processed and ready for training machine learning models.
In this project, we explore several machine learning algorithms such as Logistic Regression, Naive Bayes, Perceptron, and Support Vector Machines (SVM) to build models for text classification and sentiment analysis. The models are evaluated based on their accuracy, precision, recall, and F1-score.
The models trained in this project showcase varying performance metrics based on the chosen algorithm and feature engineering techniques. It is crucial to analyze these metrics to determine the effectiveness of the models in accurately classifying sentiments of Amazon product reviews.
The text classification and sentiment analysis project provides valuable insights into leveraging machine learning for analyzing textual data. By applying various algorithms and NLP techniques, businesses can extract meaningful information from customer reviews, enabling them to make informed decisions and enhance customer satisfaction.
This project was developed by a team of passionate data scientists and machine learning enthusiasts dedicated to exploring the application of AI in sentiment analysis and text classification tasks.
Thank you for exploring the text-classification-sentiment-analysis repository! Feel free to reach out to the developers for any inquiries or collaborations. Happy coding! 🚀