Text Analytics and Language Processing - CHS7402

Assessment 1: Quizzes / polls

Students will complete a formative assessment consisting of questions at the end of each week. After attempting the questions, they will receive immediate feedback with the correct answers and explanations. This process helps students identify knowledge gaps and reinforces learning before moving on to more advanced topics.

Quizzes / polls

Assessment 2: Portfolio

Portfolio

Assessment 1: Written Assignment

Written Assignment

Recent advances in information technology have enabled the extensive collection of textual data across various domains, driving significant developments in text analytics and natural language processing (NLP) approaches. During this module, you’ll gain a fundamental understanding of these approaches, particularly advanced techniques for processing and analysing textual data to extract meaningful insights. We will explore several widely known methods, including text classification, clustering, and sentiment analysis, and investigate typical applications and potential limitations.
We will guide you in selecting the right techniques for different text analysis challenges, weighing the strengths and weaknesses of each approach. You will have the opportunity to apply this knowledge using industry-standard tools and delve into high-profile applications such as conversational agents and information retrieval systems.

School of Computing and Engineering

<a href="https://library.hud.ac.uk/readinglists/CHS7402">https://library.hud.ac.uk/readinglists/CHS7402</a>

Computing and Information Systems

CHS7402 - Text Analytics and Language Processing

Text Analytics and Language Processing

Implement and evaluate various text mining and NLP models, including classification and clustering algorithms, using programming languages and libraries.

Demonstrate the ability to preprocess and analyse textual data using appropriate techniques and tools, ensuring the data is clean and suitable for analysis.

Recognise and describe various applications of NLP in real-world contexts, such as sentiment analysis, information retrieval, and conversational agents.

Understand key concepts in text mining and natural language processing, including techniques for text representation, feature extraction, and model evaluation.

Design and execute a comprehensive text mining or NLP project, effectively managing the project from data collection to presenting the results.