Sentiment Analysis of Nigerian Twitter Discussions on ASUU Strikes

This research project, titled "Sentiment Analysis of Nigerian Twitter Discussions on ASUU Strikes", is undertaken as a 400-level undergraduate project for the Computer Science programme and addresses a problem in computational social science requiring text analysis, sentiment mining, or social media analytics applied to a Nigerian social/political/cultural phenomenon. The Nigerian context provides specific relevance: Nigeria's vibrant social media culture (with over 30 million active social media users) and substantial public discourse on political, social and economic issues creates rich data for computational social science research with implications for policy, public opinion analysis and digital democracy. The project contributes to addressing local challenges while developing the student's competencies in research methodology, analytical thinking, technical implementation and academic communication. The scope of the project encompasses data collection from social media platforms (Twitter, Facebook, Reddit), text preprocessing, sentiment classification or topic modelling, analysis of patterns and recommendations, with deliverables including a comprehensive literature review situating the work within the existing body of knowledge, a clear statement of research objectives and questions, a defensible methodology section, presentation and analysis of findings, and a discussion linking results to implications for theory and practice. Expected outcomes include analysed dataset with sentiment classifications or topic models, visualisations of trends over time, comparison with offline indicators where applicable. The project also develops the student's skills in independent research, project planning, technical writing, presentation and defence — all foundational competencies for postgraduate study and professional careers in the Computer Science field. The methodology for this project follows a structured research approach combining computational text analytics with quantitative social science methods. Specific steps include: (1) Literature review — systematic review of existing scholarship on the topic, identifying gaps and theoretical frameworks, drawing on Nigerian and international sources. (2) Research design — operationalising the research questions into a clear study design with appropriate variables, hypotheses and analytical framework. (3) Data collection — social media data collection via APIs (Twitter API, Reddit API), web scraping where APIs are unavailable, with appropriate ethical considerations. (4) Data analysis — sentiment analysis using lexicon-based or ML approaches (VADER, TextBlob, BERT-based models), topic modelling using LDA or BERTopic, network analysis where relevant. (5) Validation and reliability checks — appropriate techniques for ensuring the credibility and dependability of the findings. (6) Synthesis and reporting — integrating findings into a coherent narrative addressing the research questions. Tools and techniques employed include Python with NLTK, spaCy, scikit-learn and Transformers; Tweepy or PRAW for API access; pandas for data manipulation; matplotlib/Plotly for visualization. The methodology balances academic rigour with practical feasibility within the constraints of an undergraduate research project (typically 3-6 months of focused work).

Assessors should evaluate this project on standard undergraduate research project criteria for the Computer Science programme: (1) Quality of literature review (15-20 marks) — depth of engagement with existing scholarship, clarity of theoretical framing, currency of sources. (2) Soundness of research design and methodology (20-25 marks) — appropriateness of method to research questions, clarity of operationalisation, defensible choices. (3) Quality of data collection and analysis (20-25 marks) — rigour of execution, appropriate analytical techniques, thoroughness of findings. (4) Quality of discussion and conclusions (15-20 marks) — depth of interpretation, linkage to broader literature, appropriateness of conclusions to findings. (5) Academic writing and presentation (10-15 marks) — clarity, grammatical correctness, formatting compliance with departmental standards, quality of references. (6) Oral defence (10-15 marks) — student's command of subject matter, ability to defend methodological choices, response to examiner questions. Common pitfalls in projects like "Sentiment Analysis of Nigerian Twitter Discussions on ASUU Strikes" include over-broad scope, insufficient methodological detail, weak engagement with prior literature, and conclusions not fully supported by findings.