🧠 Sentiment Analysis using NLP

🎯 Project Overview

This project focuses on performing Sentiment Analysis using Natural Language Processing (NLP) techniques. The goal is to classify user opinions or reviews into positive, negative, or neutral categories by processing and analyzing text data. We apply several preprocessing techniques, visualize patterns, and use ML/NLP models to predict sentiments effectively.

⚙️ Project Workflow

1️⃣ Data Preprocessing

• Cleaned and normalized text (lowercasing, removing stopwords, punctuation, and special characters)
• Tokenized sentences and applied Lemmatization for text standardization
• Converted text to numerical form using TF-IDF / Word2Vec embeddings

2️⃣ Exploratory Data Analysis (EDA)

• Visualized sentiment distribution using bar plots and word clouds
• Identified most frequent positive and negative words
• Explored relationships between text length, sentiment, and emotion intensity

3️⃣ Model Building – Machine Learning Models

• Trained models such as Logistic Regression, Naive Bayes, and SVM
• Compared model performances based on accuracy and F1-score
• Optimized hyperparameters using GridSearchCV

4️⃣ Model Building – Deep Learning (LSTM / ANN)

• Implemented an LSTM Neural Network to capture sequential dependencies in text
• Used Embedding Layer for word representations
• Optimized using Adam Optimizer and Binary Cross-Entropy Loss

5️⃣ Model Evaluation

• ✅ Accuracy
• ✅ Precision
• ✅ Recall
• ✅ F1 Score
• ✅ Confusion Matrix
• ✅ ROC-AUC Curve

🧰 Technologies & Libraries Used

• Python
• NumPy, Pandas
• Matplotlib, Seaborn, WordCloud
• Scikit-learn
• TensorFlow / Keras
• NLTK and spaCy

🚀 Project Goals

• To preprocess and clean textual data effectively
• To analyze and visualize sentiment distribution
• To build and evaluate predictive NLP models
• To classify user reviews accurately into sentiment categories

📊 Results

The project achieved strong performance across multiple models, with the LSTM model delivering the highest accuracy and interpretability. Word clouds and sentiment visualization provided valuable insights into customer opinions and feedback trends.

🏁 Conclusion

This project demonstrates the effective use of NLP and Deep Learning techniques to analyze and predict sentiments. It can be extended for real-world applications such as product reviews, social media monitoring, and feedback analysis.