Natural Language Processing for Accountants
Sentiment Analysis, Named Entity Recognition, and Text Generation for Accountants
About This Course
Course Information
This course covers Natural Language Processing (NLP) and its applications for accountants. Learn how NLP can be used to gain insights from unstructured text data such as emails, documents, reports, customer reviews, audit comments and more. Discuss applications of NLP for tasks like analysing sentiment, extracting data for financial reports, monitoring regulatory compliance and detecting anomalous behaviour. Explore tools and techniques to apply NLP within your own accounting function.
Certificate on Completion
This course is made up of videos, questions and additional reading materials and accounts for 3 units of CPD. One unit is the equivalent of one hour of learning. A certificate will be issued once you have completed all 3 units. Each unit represents one hour of learning.
Course Sections
This course is made up of the following sections:
- Natural Language Processing for Accountants (Multiple Videos)
- Quizzes and short assessments (Quiz)
- Additional reading materials on AI and Natural Language Processing for Accountants
- Course completion survey
- CPD Certificate issued once the course is completed
What You Will Learn
- Describe the five phases of NLP, from lexical analysis through to pragmatic analysis, and identify where techniques like summarisation and ChatGPT fit within that framework.
- Distinguish between the five main NLP techniques: sentiment analysis, text complexity, self-attribution bias, word cloud, and topic modelling.
- Explain how sentiment analysis uses tokenisation, lemmatisation, and stop-word removal to process text, and compare rule-based, machine learning, and hybrid approaches to scoring sentiment.
- Identify the four types of sentiment analysis (fine-grained, aspect-based, intent-based, and emotional detection) and apply each to accounting scenarios such as customer reviews, employee feedback, and investor comments.
- Evaluate six limitations of NLP, including contextual words, synonyms, sarcasm, ambiguity, errors, and slang, and assess their impact on text analytics accuracy.
- Describe how Named Entity Recognition (NER) extracts names, dates, locations, and values from unstructured documents like invoices and contracts.
- Assess the steps a firm should follow when deploying NLP text generation models, including defining brand personality, creating a style guide, and establishing ongoing monitoring and control.
Who This Course Is For
- Accountants who want to understand how NLP techniques like sentiment analysis and named entity recognition apply to financial documents, contracts, and client feedback.
- Finance and management accountants responsible for analysing unstructured text data such as annual reports, investor statements, or employee appraisals.
- Accounting professionals evaluating NLP vendors like Google, Microsoft, AWS, or Anthropic for deployment within their organisation.
- CPD learners looking to build practical awareness of text analytics without needing prior programming experience.
Prerequisites
- A basic understanding of artificial intelligence concepts (the course explains the three layers of AI but assumes general awareness of the term).
- No programming experience is required. Python libraries are referenced but not taught in depth.
- Suitable for qualified or part-qualified accountants across financial, management, and cost accounting roles.
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