The course is designed to provide a step-by-step guide on developing a sentiment analysis application, starting from the fundamentals and progressing to more advanced techniques. The course focuses on enabling participants to understand the concepts, tools, and techniques required to build an application that can analyze and classify sentiment from textual data.

• Understand what sentiment analysis is, its applications, and the significance of analyzing sentiment from textual data.

• Learn how to clean and preprocess textual data by removing noise, handling special characters, tokenization, normalization, and other preprocessing steps.

• Explore various techniques to convert textual data into numerical features suitable for sentiment analysis, such as bag-of-words, word embeddings (e.g., Word2Vec, GloVe), and TF-IDF.

• Gain knowledge of different machine learning algorithms commonly used for sentiment analysis, such as Naive Bayes, Support Vector Machines (SVM), and Neural Networks. Understand how to train, evaluate, and optimize these models.

• Learn about deep learning models like Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), and Convolutional Neural Networks (CNNs) specifically designed for sentiment analysis tasks. Gain hands-on experience building and training these models using popular frameworks like TensorFlow or PyTorch.

• Programming Fundamentals

• Basic Knowledge of Machine Learning

• Understanding of Natural Language Processing (NLP) Basics

• Data Manipulation and Analysis

• Familiarity with Machine Learning Libraries

• Introduction to Sentiment Analysis:

  • Understanding the concept of sentiment analysis and its applications.
  • Exploring different approaches and algorithms used in sentiment analysis.

• Text Preprocessing:

  • Techniques for cleaning and preprocessing textual data.
  • Removing noise, handling special characters, tokenization, and normalization.

• Feature Extraction:

  • Methods to convert textual data into numerical features suitable for analysis.
  • Exploring techniques such as bag-of-words, word embeddings (e.g., Word2Vec, GloVe), and TF-IDF.

• Machine Learning Models for Sentiment Analysis:

  • Introduction to various machine learning algorithms used for sentiment analysis, such as Naive Bayes, Support Vector Machines (SVM), and Neural Networks.
  • Understanding model training, evaluation, and optimization techniques.

• Deep Learning for Sentiment Analysis:

  • Introduction to deep learning models like Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), and Convolutional Neural Networks (CNNs) for sentiment analysis.
  • Building and training deep learning models using frameworks like TensorFlow or PyTorch.

• Model Deployment and Application Development:

  • Techniques for deploying sentiment analysis models into production.
  • Building a user-friendly application that can accept text inputs and provide sentiment analysis predictions.

• Advanced Topics:

  • Exploring advanced techniques such as transfer learning, ensemble models, or fine-tuning pre-trained models for sentiment analysis.
  • Handling domain-specific sentiment analysis challenges and considerations.