Sentiment Analysis of User Reviews

This project focuses on building a machine learning model to classify user reviews as either positive or negative, enabling efficient sentiment analysis. The project utilizes Natural Language Processing (NLP) techniques, with nltk for text preprocessing, and applies a binary classification model to accurately predict sentiment from user-generated content.

Project Overview

User reviews provide valuable insights into customer satisfaction, preferences, and areas for improvement. Analyzing the sentiment of these reviews can help businesses better understand customer feedback at scale. In this project, a binary classification model was developed to categorize reviews into positive and negative sentiments, leveraging machine learning and NLP techniques. This repository contains all necessary code, preprocessing steps, and model training workflows required to perform sentiment analysis on a dataset of user reviews.

Key Features

• Sentiment Prediction: Classifies reviews as either positive or negative using a binary classifier.
• Text Preprocessing with NLTK: Cleans and preprocesses text data by removing noise, stemming words, and eliminating stopwords.
• Machine Learning Modeling: Implements a binary classification algorithm, training and testing the model on labeled data to achieve high accuracy in sentiment prediction.

Tools and Libraries

• Python: Primary language for implementing the model and data processing.
• NLTK (Natural Language Toolkit): Used for text preprocessing, including tokenization, stopword removal, and stemming.
• Scikit-Learn: Provides various machine learning algorithms and tools for model evaluation.
• Pandas and NumPy: Used for data manipulation and numerical operations.
• Jupyter Notebook: For code exploration, testing, and visualization.

Project Workflow

1. Data Collection: Collected a dataset of user reviews, with labels indicating whether each review is positive or negative.

2. Data Preprocessing:

   • Text Cleaning: Removed non-alphabetic characters from reviews to reduce noise.
   • Tokenization: Split the text into individual words (tokens).
   • Stopword Removal: Removed common stopwords (e.g., "the," "is") using nltk's stopword list to focus on meaningful words.
   • Stemming: Reduced words to their root forms using NLTK’s PorterStemmer to standardize different forms of the same word (e.g., "running" to "run").

3. Feature Extraction:

   • Converted the preprocessed text data into numerical features using techniques such as Bag of Words (BoW) or Term Frequency-Inverse Document Frequency (TF-IDF), making the data compatible with machine learning algorithms.

4. Model Training and Evaluation:

   • Trained a binary classification model (e.g., logistic regression, support vector machine) to distinguish between positive and negative sentiments.
   • Evaluated model performance using metrics such as accuracy, precision, recall, and F1 score on a separate test set.

5. Model Optimization:

   • Performed hyperparameter tuning to enhance model performance and accuracy.

6. Prediction and Insights:

   • The trained model can now predict the sentiment of new user reviews, helping to derive actionable insights from large volumes of textual data.

Installation and Usage

Prerequisites

Make sure to install the necessary libraries:

pip install nltk scikit-learn pandas numpy

Running the Project

1. Download or Clone the Repository:

   git clone [https://github.com/ayyash1/Sentiment-prediction-using-machine-learning.git]
   cd sentiment-analysis

2. Data Preprocessing:

   • Run the preprocessing script to clean and prepare the data for model training.

3. Model Training:

   • Run the training script to train the binary classification model on the preprocessed dataset.

4. Sentiment Prediction:

   • Use the trained model to predict sentiment on new reviews or evaluate the model on test data.

Example Usage

import nltk
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import LogisticRegression

# Example of loading data, preprocessing, training, and predicting

Results and Evaluation

The model achieved a satisfactory accuracy on the test set, demonstrating its effectiveness in distinguishing positive and negative sentiments. Precision, recall, and F1 scores were also used to gauge model performance.

Future Improvements

• Incorporate Deep Learning: Experiment with advanced models such as LSTM or BERT for potentially higher accuracy.
• Fine-tune Preprocessing: Refine stopword removal and explore lemmatization to improve text normalization.
• Deploy Model: Package and deploy the model as a web app for real-time sentiment analysis on new user reviews.

Contributing

Contributions to improve this sentiment analysis model are welcome! Please create a pull request or open an issue if you have suggestions or feature requests.