MSc Data Science graduate with research interests in Natural Language Processing (NLP), Explainable AI (XAI), Trustworthy AI, and Machine Learning.

I recently completed an MSc in Data Science at the University of South Wales, United Kingdom. My research interests focus on developing transparent, interpretable, and trustworthy AI systems for analysing textual and social media data.

My MSc dissertation investigated interpretable machine learning approaches for large-scale sentiment classification using approximately 1.6 million social media posts from the Sentiment140 dataset. Through this work, I developed a strong interest in Explainable AI, Trustworthy AI, and human-centred approaches to Natural Language Processing.

• Natural Language Processing (NLP)
• Explainable AI (XAI)
• Trustworthy AI
• Machine Learning
• Text Classification
• Social Media Analytics
• Human-Centred AI

MSc Dissertation Project

Repository: sentiment-analysis

Key highlights:

• Processed and analysed approximately 1.6 million tweets from the Sentiment140 dataset.
• Developed an end-to-end NLP pipeline for sentiment classification.
• Applied preprocessing techniques including tokenisation, lemmatisation, emoji handling, and negation processing.
• Implemented and evaluated Logistic Regression, Linear SVC, Bernoulli Naive Bayes, Multinomial Naive Bayes, and SGD classifiers.
• Performed TF-IDF feature engineering using unigram and bigram representations.
• Achieved 82.6% classification accuracy using Logistic Regression.
• Investigated model interpretability and transparent machine learning approaches.

I am particularly interested in research exploring:

• Explainable NLP Systems
• Trustworthy AI
• Human-Centred AI
• Transformer-Based Language Models
• Responsible AI
• Social Media and Online Information Analysis

MSc in Data Science

University of South Wales, United Kingdom

BSc Computer Science and Engineering

North South University, Bangladesh

📧 Email: [email protected]

🔗 LinkedIn: https://linkedin.com/in/akiba-mehzaveen-mon