🐦 Twitter Sentiment Analysis

📌 Introduction

Natural Language Processing (NLP) is one of the most exciting fields in data science today. Among its many applications, sentiment analysis plays a key role in understanding public opinion, customer feedback, and brand perception.

In this project, we perform sentiment analysis on tweets to classify them as positive, negative, or neutral. Whether it's for opinion polling, brand monitoring, or developing marketing strategies — sentiment analysis is a powerful tool every data scientist should master.

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🎯 Objective

To analyze tweets using NLP techniques and machine learning models to:

• Understand the underlying sentiment of text data
• Classify tweets based on polarity (Positive, Negative, Neutral)
• Visualize and interpret results using various plots and metrics

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🧰 Project Workflow

📥 1. Data Collection

• Source: Twitter Dataset (cleaned or using Twitter API)
• Includes tweet text and corresponding sentiment labels

🔧 2. Data Preprocessing

• Lowercasing, removing special characters and URLs
• Tokenization, stopword removal, and stemming/lemmatization
• Word cloud and text length analysis

📊 3. Exploratory Data Analysis (EDA)

• Distribution of sentiments
• Frequent words by sentiment class
• Word clouds for positive, negative, and neutral tweets

🧠 4. Sentiment Classification Models

• Machine Learning Models Used:
  • Logistic Regression
  • Naive Bayes
  • Support Vector Machine (SVM)
  • Random Forest
• Metrics:
  • Accuracy
  • Precision, Recall, F1-score
  • Confusion Matrix

🌐 5. Advanced NLP (Optional)

• TF-IDF Vectorization
• Word Embeddings (e.g., Word2Vec or GloVe)
• Deep Learning models (LSTM, BERT) <<<<<<< HEAD

By Mukesh Sharma

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