This course is part of the Natural Language Processing ExpertTrack Pathway ExpertTrack
Text Classification and Sentiment Analysis
Apply NLP and machine learning techniques to classify text, interpret sentiment, and uncover meaning in real-world language data.
Duration
3 weeksWeekly study
3 hours100% online
How it worksIncluded in an ExpertTrack
Course 2 of 3
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.
Syllabus
Week 1
Text Representations for NLP
Handle Complex Vocabulary with Subword Tokenisation
Explore Byte-Pair Encoding, unigram models and subword techniques for representing complex vocabulary and handling unseen words in practical NLP scenarios.
Adapt Tokenisation for Diverse Language Data
Examine adaptive and transformer-based approaches for processing streaming text, low-resource languages, morphologically rich language and changing vocabulary.
Capture Meaning with Richer Text Representations
Move beyond word-level features with character, subword, hybrid and sentence embeddings, alongside similarity measures for identifying semantic relationships.
Week 2
From Sentiment Rules to Transformer Models
Detect Sentiment with Lexicons and Rules
Establish sentiment polarity using lexicons, preprocessing choices, scoring methods and heuristic rules within a practical sentiment analysis workflow.
Develop Machine Learning Sentiment Classifiers
Train classical classifiers while addressing dimensionality, topic patterns, class imbalance and evaluation considerations in sentiment modelling.
Advance Sentiment Analysis with Deep Learning
Extend sentiment classification through LSTMs, GRUs, attention mechanisms, pretrained transformers and modern few-shot and zero-shot approaches.
Week 3
Sentiment Analysis Across Real-World Contexts
Track Sentiment Trends and Event Impact
Investigate how opinions change over time, identify sudden sentiment shifts and assess the influence of events on public discourse.
Explore Sentiment Across Aspects and Languages
Examine fine-grained opinions through aspect-based analysis before extending sentiment modelling to multilingual and cross-lingual settings.
Promote Responsible Sentiment Analysis
Address bias, classification errors, fairness and accountability when sentiment systems are used in sensitive and high-impact contexts.
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Learning on this course
On every step of the course you can meet other learners, share your ideas and join in with active discussions in the comments.
What will you achieve?
By the end of the course, you‘ll be able to...
- 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.
Who is the course for?
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.
What software or tools do you need?
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.
Who will you learn with?
Who developed the course?
Edureka
Edureka is a global online learning platform offering industry-focused training in emerging technologies such as Cloud, AI, GenAI, Data Science, DevOps, and Cybersecurity, helping professionals upskill and advance their careers.
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