Wiphada Wettayaprasit
Prince of Songkla University

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Journal : Indonesian Journal of Electrical Engineering and Computer Science

A new approach for extracting and scoring aspect using SentiWordNet Tuan Anh Tran; Jarunee Duangsuwan; Wiphada Wettayaprasit
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i3.pp1731-1738

Abstract

Aspect-based online information on social media plays a vital role in influencing people’s opinions when consumers concern with their decisions to make a purchase, or companies intend to pursue opinions on their product or services. Determining aspect-based opinions from the online information is necessary for business intelligence to support users in reaching their objectives. In this study, we propose the new aspect extraction and scoring system which has three procedures. The first procedure is normalizing and tagging part-of-speech for sentences of datasets. The second procedure is extracting aspects with pattern rules. The third procedure is assigning scores for aspects with SentiWordNet. In the experiments, benchmark datasets of customer reviews are used for evaluation. The performance evaluation of our proposed system shows that our proposed system has high accuracy when compared to other systems.
A More Reliable Step Counter using Built-in Accelerometer in Smartphone Win Win Myo; Wiphada Wettayaprasit; Pattara Aiyarak
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 2: November 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i2.pp775-782

Abstract

Step counter, being an active area of human daily physical activity, is an essential role in human activity determination research. As the current smartphones come with many different sensors and powerful processing capabilities, the step counting using built-in sensors in a smartphone is increasingly becoming a vital factor among many researchers. However, the step counting with a smartphone has still challenging due to many different walking behaviors and mobile phone positions. In this study, we introduce a more reliable step counter’s technique using Accelerometer sensor in a smart phone. The objective of this study is to get the accurate steps of three different walking activities in four different mobile positions. In order to achieve this, a new reliable technique based on peak is attracting considerable in our work using average acceleration. The experimental result shows 99.02% as an overall step counting performance that the proposed method reliably detects the steps under varying walking speed in different devices modes. This result is encouraging to facilitate among of the complex walking activities using built-in sensors in smartphone.