Text Classification and Sentiment Analysis

Turn text into predictions and insight with NLP

Once text has been cleaned and structured, the next challenge is teaching machines to interpret what it means.

On this course, you’ll move beyond preprocessing and apply Natural Language Processing (NLP) and machine learning techniques to classify text, analyse sentiment, and extract useful insight from language data.

Start by framing common language problems, including text classification, sentiment analysis, and topic labelling.

You’ll explore how labelled datasets are created and use features such as bag-of-words, TF-IDF, and n-grams to represent text for predictive models.

Interpret sentiment in real-world text

Compare lexicon-based, rule-based, and supervised approaches to sentiment analysis.

You’ll examine polarity and subjectivity before applying these techniques to sources such as reviews and social media, where language can be noisy, nuanced, and context dependent.

Capture meaning with word embeddings

Move beyond sparse representations and explore dense word embeddings including Word2Vec and GloVe.

You’ll investigate semantic similarity and see how vector representations help models capture relationships between words and meaning.

Diagnose and improve NLP models

Use error analysis to understand why text models make mistakes and explore challenges such as negation, sarcasm, rare words, and domain shift.

You’ll then bring your learning together by creating an end-to-end sentiment analysis application using a real text corpus.

By the end, you’ll be able to classify and interpret text with greater confidence, preparing you to progress into deep learning and transformer-based NLP.

This course is for aspiring NLP engineers, data scientists, language technology practitioners, analysts, and developers ready to move from text preprocessing into classification and sentiment analysis. Basic NLP, Python, and TF-IDF knowledge is recommended.

No prior sentiment analysis experience is required, although a basic familiarity with Python and core NLP concepts will help you follow the demonstrations. The course introduces the tools and setup you need from the beginning, including the Python libraries, pretrained models and datasets used in the practical activities. You will need access to a suitable computer, a stable internet connection and the relevant online accounts to follow the hands-on exercises.

This course is for aspiring NLP engineers, data scientists, language technology practitioners, analysts, and developers ready to move from text preprocessing into classification and sentiment analysis. Basic NLP, Python, and TF-IDF knowledge is recommended.

• Apply subword, adaptive, and character-level tokenisation techniques to represent complex and diverse language data.
• Use sentence embeddings and similarity measures to identify semantic relationships between texts.
• Compare rule-based, classical machine-learning, and deep-learning approaches to sentiment classification.
• Develop sentiment analysis solutions using pretrained and fine-tuned transformer models.
• Evaluate sentiment systems across temporal, aspect-based, and multilingual contexts while addressing bias and fairness.