The study course is primarily aimed at developing advanced and highly specialized proficiency level competences and skills of the students mastering study programs in humanities, interdisciplinary STEM+ based, and information technology. The study course is envisioned for post-graduate students with the basic knowledge of natural language processing (NLP) willing to advance their competence in sentiment analysis and textual data processing for a variety of applied industry-related tasks. Students will learn to classify unstructured and semi-structured data to determine sentiment polarity (i.e., either positive, negative, or neutral) with the help of free and commercial tools that they will customize for their own research, learning, and occupational needs. Students learn to retrieve and select sentiment-related characteristics and contextual features using relevant models and to assess the impact of the emotion-related words on the overall sentiment of the analysed text. Students will develop skills to extract the semantic information from the text, to analyse, classify and evaluate it in terms of sentiment for improving customer experience and quality assurance purposes. Students will master speech tagging, noun phrase extraction, emotion detection, and sentiment analysis, and will address such notions as polarity, intentions and subjectivity by practically working with Python and its dedicated libraries for sentiment analysis, e.g., NLTK and TextBlob. In the practical assignments, students will implement lexicon-based sentiment analysis (e.g., using the VADER (Valence Aware Dictionary and sEntiment Reasoner) lexicon), use pre-trained models for sentiment identification (e.g., the RoBERTa model) and other solutions such as Matplotlib library for the visualization and evaluations of sentiment analysis results.
Given the added value sentiment analysis can ensure in the analysis of marketing data, e-commerce, public opinion polling, business intelligence, policy planning, education efficiency assessment, and other fields, the competencies and skills students will develop within the study course will contribute to their professional practices and employability and help them solve topical industry challenges.