Emoji’s sentiment score estimation using convolutional neural network with multi-scale emoji images

Emojis are any small images, symbols, or icons that are used in social media. Several well-known emojis have been ranked and sentiment scores have been assigned to them. These ranked emojis can be used for sentiment analysis; however, many new released emojis have not been ranked and have no sentiment score yet. This paper proposes a new method to estimate the sentiment score of any unranked emotion emoji from its image by classifying it into the class of the most similar ranked emoji and then estimating the sentiment score using the score of the most similar emoji. The accuracy of sentiment score estimation is improved by using multi-scale images. The ranked emoji image data set consisted of 613 classes with 161 emoji images from three different platforms in each class. The images were cropped to produce multi-scale images. The classification and estimation were performed by using convolutional neural network (CNN) with multi-scale emoji images and the proposed voting algorithm called the majority voting with probability (MVP). The proposed method was evaluated on two datasets: ranked emoji images and unranked emoji images. The accuracies of sentiment score estimation for the ranked and unranked emoji test images are 98% and 51%, respectively

The Need for Knowledge Acquisition Tools by Small Business Entrepreneurs: The Case of Financial Knowledge Acquisition of Restaurant Entrepreneurs

This study investigates the relationship between the need for knowledge acquisition tools and the level of knowledge acquisition in the context of financial knowledge, as well as the use of different knowledge acquisition tools by small restaurant entrepreneurs. This study involves two phases.  In phase 1, a qualitative approach was employed using an in-depth interview method. In-depth interviews were conducted with 9 small restaurant entrepreneurs in order to obtain insight into their level of knowledge acquisition in the context of financial knowledge, their level of use for various knowledge acquisition tools (websites, Facebook, Line, YouTube, blogs, and mobile phone application), and their need for knowledge acquisition tools. Interview responses are explored using a thematic content analysis. In phase 2, a quantitative approach was employed using a survey questionnaire, collecting responses from 320 small restaurant entrepreneurs. Through data generated from questionnaire responses,   a  hierarchical  multiple   regression  was  used  to  determine   the relationship between the need for knowledge acquisition tools, and the level of knowledge acquisition in the context of financial knowledge. Cluster analysis techniques were used to identify the level of use for various knowledge acquisition tools, distributing entrepreneurs into different levels. The Cochran Q-test and McNemar test were used to study the difference in use of each knowledge acquisition tool by these entrepreneurs. The result suggests that small restaurant entrepreneurs with a greater need for financial knowledge acquisition tools tentatively acquire more financial knowledge. There is a difference in the level of use of knowledge acquisition tools by entrepreneurs, while the tools most commonly used for gaining financial knowledge are websites and Facebook

Solving the Difficult Problem of Topic Extraction in Thai Tweets

We tackled in this study the difficult problem of topic extraction in Thai tweets on the country’s historic flood in 2011. After using Latent Dirichlet Allocation (LDA) to extract the topics, the first difficulty that faced us was the inaccuracy the word segmentation task that affected our interpretation of the LDA result. To solve this, we refined the stop word list from the LDA result by removing uninformative words caused by the word segmentation, which resulted to a more relevant and comprehensible outcome. With the improved results, we then constructed a rule-based categorization model and used it to categorize all the collected tweets on a per-week scale to observe changes in tweeting trend. Not only did the categories reveal the most relevant and compelling topics that people raised at that time, they also allowed us to understand how people perceived the situations as they unfold over tim

The study of mobile learning readiness in rural area: case of North-Eastern of Thailand

Thai government is now encouraging each rural sub-district (Tambon) to be able to gain the income from selling their One Tambon One Product (OTOP) through the ecommerce. To be able to do that, local community members should have a knowledge of e-commerce. The easiest way would be learning from a mobile phone (mobile learning). In this paper, we study the readiness of mobile learning in the rural sub-district area. We collected data from 164 participants from the OTOP seminar in the Northeastern of Thailand. From the statistical data shows that 88.4% of the Thais in rural subdistrict have smart phones that connect to an internet most of the time. Most of them use mobile phones to call, do social network and also sending pictures or videos on daily routine. Even they do not use mobile phones for learning, but 89.5% show that they are willing and intending to learn through mobile phones. The mobile learning is more flexible and can be used anytime and anywhere which is appropriate to their life style. The study of the mobile learning readiness let us know that Thais in rural sub-district should learn e-commerce content from mobile phones. The next challenge would be how to design the content for Thais in the rural sub-district.info:eu-repo/semantics/publishedVersio

E-Commerce Competence Assessment Mobile Application Development for SMEs in Thailand

The objectives of this study are to develop e-Commerce self-assessment application based on research and to evaluate the adoption of the system by using the Technology Acceptance Model (TAM) which are designed for SMEs self-assessment and the government agencies are able to retrieve the report. The system development deployed Microsoft .NET CORE Technology with C# language, HTML/CSS/JS for Mobile Web Development. All data will store on Aurora RDS. The web deployed on Amazon public cloud that will automatic scale when a lot of traffic arrived. Since the indicators from the research have been developed as an application, users will be able to use it conveniently anytime and anywhere. The system will help entrepreneurs realize the level of ecommerce knowledge of their own in various areas and help them to know what needs to be improved and more importantly, government officials can allocate budget for training in order to improve entrepreneurs’ performance. The evaluation result of system adoption in 30 cosmetic and supplementary food entrepreneurs showed that for Perceive Usefulness, the use of application systematically revealed the strength, weakness and potential of themselves and was beneficial for self-assessment in online selling skills, xÌ„ = 4.50 equally. Perceived Ease of Use, it is very easy to do self-assessment through application, xÌ„ = 4.63. Attitude Toward Using, in general, the attitude toward using the application was positive, xÌ„ = 4.73. Behavioral Intention to Use, the intention to use this application in the future, xÌ„ = 4.57 and 100% of 30 respondents were interested to use the system

Development of a User-Centric Bridge Visual Defect Quality Control Assisted Mobile Application: A Case of Thailand’s Department of Highways

Digital innovations have changed the way many industries operate, but the construction industry has been slow to adopt these technologies. However, challenges such as low productivity, project overruns, labor shortages, and inefficient performance management have motivated Thailand’s Department of Highways to adopt digital innovations to build a competitive advantage. Because this industry requires a large work force, obstacles to collaboration can result in ineffective project management. We aimed to improve collaboration on bridge inspections that typically requires the involvement of many people, personal judgement, and extensive travel to survey bridges across the country. One major challenge is to standardize human judgement. To address these challenges, we developed a user-centric bridge visual defect quality control mobile application to improve collaboration and assist field technicians to conduct visual defect inspection. Our results can be used as a case study for other construction firms to embrace digital transformation technologies. This research also demonstrates the new-product development process using the new technology in known markets innovation development and technology acceptance model. We offer several recommendations for future research, including other infrastructure applications

Innovative Mobile Application for Measuring Big Data Maturity: Case of SMEs in Thailand

A Big Data maturity model (BDMM) is one of the key tools for Big Data assessment and monitoring, and a guideline for maximizing the usage and opportunity of Big Data in organizations. The development of a BDMM for SMEs is a new concept and is challenging in terms of development, application, and adoption. This article aims to create the novel online adaptive BDMM via responsive web application for SMEs. We develop the BDMM API and a responsive web application for easy access via mobile phone. We developed a model by analyzing the factors impacting the success of implementing Big Data Analytics (BDA) in SMEs based on literature reviews. The model was verified by conducting a survey of 180 SMEs in Thailand, interviewed against four extracted domains. Then, the scoring and classified levels for the model was developed through Latent Class Analysis (LCA) to depict four levels of each domain and four final maturity levels to create an adaptive model. As the experimental results with 33 users including executive officers, managers, IT and data analytic officers .The user acceptance for our mobile application using TAM indicates that executive officers group and non-executive group satisfied perceived usefulness, perceived ease of use, and intention to use factor. Use cases of the application include SMEs monitoring for their Big Data Analytics capability for improvement, and the Government Agency providing proper support on SMEs’ level of competency

Innovative Mobile Application for Measuring Big Data Maturity: Case of SMEs in Thailand

A Big Data maturity model (BDMM) is one of the key tools for Big Data assessment and monitoring, and a guideline for maximizing the usage and opportunity of Big Data in organizations. The development of a BDMM for SMEs is a new concept and is challenging in terms of development, application, and adoption. This article aims to create the novel online adaptive BDMM via responsive web application for SMEs. We develop the BDMM API and a responsive web application for easy access via mobile phone. We developed a model by analyzing the factors impacting the success of implementing Big Data Analytics (BDA) in SMEs based on literature reviews. The model was verified by conducting a survey of 180 SMEs in Thailand, interviewed against four extracted domains. Then, the scoring and classified levels for the model was developed through Latent Class Analysis (LCA) to depict four levels of each domain and four final maturity levels to create an adaptive model. As the experimental results with 33 users including executive officers, managers, IT and data analytic officers .The user acceptance for our mobile application using TAM indicates that executive officers group and non-executive group satisfied perceived usefulness, perceived ease of use, and intention to use factor. Use cases of the application include SMEs monitoring for their Big Data Analytics capability for improvement, and the Government Agency providing proper support on SMEs’ level of competency