Sentiment Analysis Using Python

Week 1: Introduction and Basic Techniques

 • Module 1.1: Introduction to Sentiment Analysis

 ◦ Definition and applications of sentiment analysis

 ◦ Overview of sentiment analysis techniques

 • Module 1.2: Setting Up the Environment

 ◦ Installing Python and necessary libraries (NLTK, TextBlob, scikit-learn)

 ◦ Introduction to Jupyter Notebooks

 • Module 1.3: Text Preprocessing

 ◦ Tokenization, stop words removal, and stemming

 ◦ Using NLTK for text preprocessing

 • Module 1.4: Basic Sentiment Analysis

 ◦ Using TextBlob for simple sentiment analysis

 ◦ Analyzing sentiment of sample texts

Week 2: Advanced Techniques and Applications

 • Module 2.1: Machine Learning for Sentiment Analysis

 ◦ Overview of machine learning algorithms for sentiment analysis

 ◦ Training a sentiment analysis model using scikit-learn

 • Module 2.2: Working with Real-World Data

 ◦ Collecting and preparing datasets (e.g., tweets, reviews)

 ◦ Feature extraction and vectorization (TF-IDF, word embeddings)

 • Module 2.3: Model Evaluation and Optimization

 ◦ Evaluating model performance (accuracy, precision, recall)

 ◦ Hyperparameter tuning and model optimization

 • Module 2.4: Project and Future Trends

 ◦ Developing a sentiment analysis project

 ◦ Exploring advanced topics (deep learning, transformer models)

 ◦ Future trends in sentiment analysis