NLP for Sentiment Analysis

Sentiment Analysis of Customer Reviews Using Natural Language Processing and Machine Learning

Sentiment Analysis is a Natural Language Processing (NLP) technique used to identify and classify opinions expressed in textual data. This project analyzes customer reviews and predicts whether the sentiment is Positive or Negative using Machine Learning techniques.

The system preprocesses textual data, converts it into numerical features using TF-IDF (Term Frequency-Inverse Document Frequency), and trains a Logistic Regression classifier to perform sentiment prediction. This project demonstrates how NLP and Machine Learning can be applied to automate customer feedback analysis and support data-driven decision-making.

Features

• Customer review sentiment classification
• Text preprocessing and cleaning
• Tokenization and stop-word removal
• TF-IDF feature extraction
• Logistic Regression classification model
• Interactive sentiment prediction
• Model performance evaluation
• Easy-to-understand implementation using Python

Objectives

• Collect customer review data.
• Preprocess and clean textual data.
• Convert text into numerical features.
• Train a machine learning model.
• Predict customer sentiment.
• Evaluate model performance.
• Visualize classification results.

Technologies Used

• Python
• TensorFlow
• Pandas
• NumPy
• NLTK
• Scikit-learn
• Matplotlib
• Google Colab / Jupyter Notebook

Machine Learning Algorithm

• Logistic Regression

Dataset

Dataset Name: IMDb Movie Review Dataset

The dataset is provided by TensorFlow Keras and contains movie reviews labeled as Positive or Negative.

For this project, a reduced dataset containing 10,000 reviews is included in the repository.

Location:

Dataset/
└── IMDb_Movie_Review_Dataset.csv

Project Workflow

IMDb Movie Reviews
        │
        ▼
Data Collection
        │
        ▼
Text Preprocessing
        │
        ▼
TF-IDF Vectorization
        │
        ▼
Model Training
        │
        ▼
Sentiment Prediction
        │
        ▼
Performance Evaluation

Sample Prediction

Input

The movie was absolutely amazing and I enjoyed every scene.

Output

Positive

Input

The movie was boring and a complete waste of time.

Output

Negative

Results

The following outputs were generated after training and evaluating the Logistic Regression model on the IMDb Movie Review Dataset.

Model Accuracy

Classification Report

Confusion Matrix

Sample Prediction Output

The model successfully classifies IMDb movie reviews into Positive and Negative sentiments using TF-IDF Vectorization and Logistic Regression. The results include the model accuracy, detailed classification metrics, confusion matrix, and sample prediction output.

Performance Evaluation

The trained model is evaluated using:

• Accuracy
• Precision
• Recall
• F1-Score

The project achieves good sentiment classification performance using TF-IDF feature extraction and Logistic Regression.

Advantages

• Automates sentiment classification.
• Reduces manual analysis effort.
• Fast and efficient prediction.
• Easy to implement and understand.
• Suitable for customer feedback analysis.
• Can be extended for real-world business applications.

Limitations

• Limited to binary sentiment classification.
• May not correctly interpret sarcasm or irony.
• Performance depends on dataset quality.
• Domain-specific language may reduce prediction accuracy.

Applications

• Product review analysis
• Customer feedback systems
• Social media monitoring
• Brand reputation management
• Market research
• Opinion mining

Future Enhancements

• Multiclass sentiment classification (Positive, Negative, Neutral)
• Deep Learning models (LSTM, GRU)
• Transformer-based models (BERT)
• Multilingual sentiment analysis
• Real-time social media sentiment monitoring
• Web application deployment using Flask or Streamlit

License

This project is licensed under the MIT License.