Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Wiphada Wettayaprasit
Prince of Songkla University
Computer Science & IT Engineering

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
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.
A novel meta-embedding technique for drug reviews sentiment analysis Aye Hninn Khine; Wiphada Wettayaprasit; Jarunee Duangsuwan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i4.pp1938-1946

Abstract

Traditional word embedding models have been used in the feature extraction process of deep learning models for sentiment analysis. However, these models ignore the sentiment properties of words while maintaining the contextual relationships and have inadequate representation for domainspecific words. This paper proposes a method to develop a meta embedding model by exploiting domain sentiment polarity and adverse drug reaction (ADR) features to render word embedding models more suitable for medical sentiment analysis. The proposed lexicon is developed from the medical blogs corpus. The polarity scores of the existing lexicons are adjusted to assign new polarity score to each word. The neural network model utilizes sentiment lexicons and ADR in learning refined word embedding. The refined embedding obtained from the proposed approach is concatenated with original word vectors, lexicon vectors, and ADR feature to form a meta-embedding model which maintains both contextual and sentimental properties. The final meta-embedding acts as a feature extractor to assess the effectiveness of the model in drug reviews sentiment analysis. The experiments are conducted on global vectors (GloVE) and skip-gram word2vector (Word2Vec) models. The empirical results demonstrate the proposed meta-embedding model outperforms traditional word embedding in different performance measures.
Co-Authors Aye Hninn Khine Jarunee Duangsuwan Pattara Aiyarak Tuan Anh Tran Win Win Myo