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All Journal JOIV : International Journal on Informatics Visualization
Goh, Hui-Ngo
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Computer Science & IT

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Identifying Fraud Sellers in E-Commerce Platform Anand, Lovesh; Goh, Hui-Ngo; Ting, Choo-Yee; Quek, Albert
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.3479

Abstract

The identification of fake reviews in e-commerce is crucial as they might impact the purchasing decisions and overall satisfaction of buyers. This work investigates the effectiveness of machine learning and transformer-based models for detecting fake reviews on the Amazon Fake Review Labelled Dataset. The dataset contains 20,000 computer-generated and 20,000 original reviews across various product categories with no missing values. In this study, machine learning and transformer-based models were compared, revealing that transformer-based models outperformed in terms of accuracy in detecting fake reviews, achieving an accuracy of 98% with the DistilBERT model. Additionally, this work too examines the impact of word embedding on machine learning models in enhancing fake review detection accuracy. The results show that the word embedding model Word2Vec displays notable improvements, achieving accuracies of 92% with SVM and 90% with Random Forest and Logistic Regression. Furthermore, a comparison study being carried out on comparing transformer models from previous work, which utilized the same full dataset, it was found that the DistilBERT model produced comparable accuracy despite its lighter architecture. In summary, this study underscores the effectiveness of transformer-based models and machine learning models in detecting fake reviews while at the same time highlighting the importance of word embedding techniques in enhancing the performance of machine learning models. With this work, it is hope that it would contribute to combating fake reviews and fostering trust in e-commerce platforms.
EmoStory: Emotion Prediction and Mapping in Narrative Stories Too, Seng-Wei; See, John; Quek, Albert; Goh, Hui-Ngo
JOIV : International Journal on Informatics Visualization Vol 7, No 3-2 (2023): Empowering the Future: The Role of Information Technology in Building Resilien
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3-2.2335

Abstract

A well-designed story is built upon a sequence of plots and events. Each event has its purpose in piquing the audience's interest in the plot; thus, understanding the flow of emotions within the story is vital to its success. A story is usually built up through dramatic changes in emotion and mood to create resonance with the audience. The lack of research in this understudied field warrants exploring several aspects of the emotional analysis of stories. In this paper, we propose an encoder-decoder framework to perform sentence-level emotion recognition of narrative stories on both dimensional and categorical aspects, achieving MAE=0.0846 and 54% accuracy (8-class), respectively, on the EmoTales dataset and a reasonably good level of generalization to an untrained dataset. The first use of attention and multi-head attention mechanisms for emotion representation mapping (ERM) yields state-of-the-art performance in certain settings. We further present the preliminary idea of EmoStory, a concept that seamlessly predicts both dimensional and categorical space in an efficient manner, made possible with ERM. This methodology is useful in only one of the two aspects is available. In the future, these techniques could be extended to model the personality or emotional state of characters in stories, which could benefit the affective assessment of experiences and the creation of emotive avatars and virtual worlds
Forum Text Processing and Summarization Mak, Yen-Wei; Goh, Hui-Ngo; Lim, Amy Hui-Lan
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.2279

Abstract

Frequently Asked Questions (FAQs) are extensively studied in general domains like the medical field, but such frameworks are lacking in domains such as software engineering and open-source communities. This research aims to bridge this gap by establishing the foundations of an automated FAQ Generation and Retrieval framework specifically tailored to the software engineering domain. The framework involves analyzing, ranking, performing sentiment analysis, and summarization techniques on open forums like StackOverflow and GitHub issues. A corpus of Stack Overflow post data is collected to evaluate the proposed framework and the selected models. Integrating state-of-the-art models of string-matching models, sentiment analysis models, summarization models, and the proprietary ranking formula proposed in this paper forms a robust Automatic FAQ Generation and Retrieval framework to facilitate developers' work. String matching, sentiment analysis, and summarization models are evaluated, and F1 scores of 71.31%, 74.90%, and 53.4% were achieved. Given the subjective nature of evaluations in this context, a human review is used to further validate the effectiveness of the overall framework, with assessments made on relevancy, preferred ranking, and preferred summarization. Future work includes improving summarization models by incorporating text classification and summarizing them individually (Kou et al, 2023), as well as proposing feedback loop systems based on human reinforcement learning. Furthermore, efforts will be made to optimize the framework by utilizing knowledge graphs for dimension reduction, enabling it to handle larger corpora effectively
Co-Authors Anand, Lovesh Lim, Amy Hui-Lan Mak, Yen-Wei Quek, Albert See, John Ting, Choo-Yee Too, Seng-Wei