COURSE OVERVIEW

 This practical and highly-interactive course includes various practical sessions and exercises. Theory learnt will be applied using our state-of-the-art simulators. 

This course is designed to provide participants with a detailed and up-to-date overview of Sentiment Analysis using Python. It covers the sentiment analysis including its applications, challenges and analysis techniques; the natural language processing (NLP), python environment and text preprocessing for sentiment analysis; the text data structures, word frequency and n-gram analysis, word cloud visualization and sentiment trends in text; the sentiment lexicons, TextBlob for sentiment scoring, rule-based classification using NLP libraries and pros and cons of rule-based methods; and the conversion of text to numerical representation, bag-of-words (BoW) model, term frequency-inverse document frequency (TF-IDF) and word embeddings. 

 

Further, the course will also discuss the labeled datasets, data into training and testing sets; the building of a sentiment classification model and evaluating model accuracy; the sentiment classification with naïve bayes, sentiment analysis using support vector machines (SVM) and deep learning for sentiment analysis; and the word embeddings for deep learning models and sentiment analysis using recurrent neural networks (RNN). 

 

During this interactive course, participants will learn the long short-term memory networks (LSTM) for sentiment classification and bidirectional LSTM (BiLSTM) for text classification; the applications of transformers in real-world sentiment analysis and the bidirectional encoder representations from transformers (BERT); the sentiment analysis using GPT-3 and ChatGPT and transfering learning and multiple languages using AI; the customer feedback for sentiment trends, AI-driven sentiment monitoring in social media, product reviews, brand reputation and crisis detection from sentiment trends; the sentiment analysis in finance and stock market predictions and sentiment analysis models in production; the implementation of bias and fairness in sentiment analysis models; handling sarcasm and negations in sentiment detection and the responsible AI use in sentiment analysis; the advances in AI-driven sentiment analysis, real-time sentiment tracking using AI and sentiment analysis with multimodal AI; and the impact of generative AI on sentiment detection.