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All Journal Bulletin of Electrical Engineering and Informatics JOIV : International Journal on Informatics Visualization
Fahad, Nafiz
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Computer Science & IT Electrical & Electronics Engineering

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A machine and DL approach for classifying customer sentiments from online shopping reviews in Bangla text Arifur Rahman Rejuan, Md.; Assaduzzaman, Md; Fahad, Nafiz; Jakir Hossen, Md.; Rahmatul Kabir Rasel Sarker, Md.
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9122

Abstract

Due to the widespread availability of the internet all across the world, people prefer shopping online rather than going to a shop. There are various online marketplaces available in Bangladesh, like Daraz, Pickaboo, Rokomari, Othoba, Bikroy, Food Panda, and Robi Shop. With the increasing quantity of customers on online shopping platforms, the number of product reviews also increases with it. Data is classified utilizing machine learning (ML), deep learning (DL), transfer learning, and other data mining algorithms to facilitate the customer’s comprehension of the primary subject of the review before making a purchase. Natural language processing techniques are employed to categorize data in any given language for such issues. There are no Bengali shopping review datasets available on online sites. So, we manually collected a dataset of 2,600 reviews. In this paper, reviews are classified into 5 categories (satisfied, very satisfied, not satisfied, fairly satisfied, and satisfied but delivery problem). DL (long short-term memory (LSTM) and convolutional neural network (CNN)) and ML (support vector machine (SVM), random forest (RF), gradient boosting (GB), and extreme gradient boosting (XGBoost)) model have been applied. Among the DL models, CNN has the best accuracy (91.27%), and the RF classifier provides the highest accuracy (84.39%) out of all the ML models.
Visual Analytic for Traffic Impact Assessment Chan, Jia Chun; Fahad, Nafiz; Goh, Kah Ong Michael; Tee, Connie
JOIV : International Journal on Informatics Visualization Vol 8, No 3 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3.2314

Abstract

This study strives to promote the state of traffic impact assessment through high-end visual analytics by incorporating spatial and temporal data visualization to enhance traffic management. Based on a dataset on traffic flow at three major intersections, we married data cleaning, integration, and transformation to set out for a detailed visual analysis. Thus, the critical materials comprise the traffic count in multiple lanes, vehicle types, and saturation flow rates to understand the road network's capacity. They essentially explored the traffic volume variations daily and hourly and pattern identification using heat maps, parallel coordinate charts, and bar plots. Thus, the findings expose the remarkable traffic volume and pattern differences by distinguishing peak and off-peak hours on weekdays and weekends. The level of service at each junction was determined by the volume-to-capacity ratio, identifying potential congested areas. As such, this work points to the importance of further improvements to visual analytic techniques to accurately predict traffic patterns and evaluate traffic management strategies effectively. Predictive models based on visual analytic findings can pave the way for proactive traffic control and congestion mitigation, making urban traffic management more efficient and safer. The current study provides a scaffold for additional exploration of the above-detailed methods and their penal outcomes in urban development planning and policy provision in terms of developing sustainable traffic control strategies and real-time decision-making improvements.
Co-Authors Arifur Rahman Rejuan, Md. Assaduzzaman, Md Chan, Jia Chun Goh, Kah Ong Michael Jakir Hossen, Md. Rahmatul Kabir Rasel Sarker, Md. Tee, Connie