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Sentiment analysis of the awareness of environmental sustainability
(2024-01-01)
Kularbphettong, Kunyanuth
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Roonrakwit, Pattarapan
;
Boonseng, Chongrag
This study examines the sentiment analysis of awareness of environmental sustainability. Environmental sustainability is the responsible management and utilization of Earth's natural resources to meet the needs of the present generation and ensure that future generations will access those resources. The awareness of environmental sustainability has been growing globally as people, businesses, and governments recognize the importance of preserving the planet for current and future generations. Sentiment analysis of environmental sustainability involves evaluating opinions, attitudes, and emotions expressed in texts related to environmental sustainability, and analyzing sentiment can provide insights into public perception, awareness, and engagement with environmental issues. This exploratory study's primary goal is to conduct social media opinion mining in the context of Thai people's environmental sustainability. The paper presented how to build a model of sentiment analysis with linguistic analysis, including data preprocessing steps, feature extraction, and model constructions. The techniques used in this research include Logistic Regression, Random Forests, Support Vector Machine, Word Segmentation and Bag of Words. The result shows that the model is able to categorize sentiment analysis opinions in the sustainability context primarily in positive terms. The positive sentiments suggest a sustained, long-term shift in awareness, or they might be influenced by specific events or trends. However, positive sentiment analysis results are expressed towards environmental sustainability initiatives, such as renewable energy projects, waste reduction efforts, or conservation programs. Moreover, public awareness plays a crucial role in influencing individual behavior, corporate practices, and government policies towards a more sustainable and environmentally conscious future.
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Assessing the potential of ionosonde for forecasting post-sunset equatorial spread F: an observational experiment in Southeast Asia
(2023-12-01)
Abadi, Prayitno
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Ali Ahmad, Umar
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Otsuka, Yuichi
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Jamjareegulgarn, Punyawi
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Almahi, Alif
The occurrence of equatorial spread F (ESF) has the potential to detrimentally impact space-based technological systems. This study investigates the utility of ionosondes in forecasting the incidence of post-sunset ESF in the zonal direction, utilizing observational data obtained from four ionosondes located near the magnetic Equator in Southeast Asia. Data were collected during the equinox seasons (March–April and September–October) between 2003 and 2020. To establish a relationship between the probability of post-sunset ESF occurrence and the evening vertical plasma drift (v), a logistic regression model was employed. Post-sunset ESF occurrence is defined as the presence of ESF during the time window between 19:00 and 21:00 LT, while v is derived from the average time derivative of virtual heights during the interval from 18:30 to 19:00 LT. Results indicate that the probability of post-sunset ESF occurrence approaches zero, signifying that ESF is unlikely to develop when v is negative. Conversely, when v exceeds 30 m/s, the probability of post-sunset ESF occurrence surpasses 0.87, indicating that ESF occurs almost invariably. The likelihood of post-sunset ESF occurrence reaches 1 when v equals or exceeds 40 m/s. Utilizing this model, the study determined that a single ionosonde positioned at the Equator can effectively forecast the incidence of post-sunset ESF up to a longitudinal distance of 30° from its location. The accuracy of ionosondes in predicting post-sunset ESF occurrence above their respective locations is approximately 0.80, with a 10% decrease in accuracy when forecasting ESF occurrence at longitudinal distances of 30°. In conclusion, this study enhances our understanding of the link between the evening vertical plasma drift and the manifestation of post-sunset ESF by leveraging ionosonde data. Furthermore, it provides valuable insights into the recommended coverage range of ionosondes for predicting post-sunset ESF occurrence in the zonal direction, which can be employed to fortify regional space weather services. Graphical Abstract: [Figure not available: see fulltext.].
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A deep learning model for predicting buy and sell recommendations in stock exchange of Thailand using long short-term memory
(2019-02-01)
Sanboon, Thaloengpattarakoon
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Keatruangkamala, Kamol
;
Jaiyen, Saichon
Nowadays, the stock price prediction has been one of the most challenging problem to the AI research community. Most prediction techniques concentrate on forecasting the future prices of stocks based on conventional Machine learning techniques. However, these techniques cannot capture long term dependencies in stock price data. Therefore, they cannot consider the relation between the current predicted data and the previous data in stock data. This research adopts deep learning techniques for predicting buy and sell recommendations in Stock Exchange of Thailand using Long Short-Term Memory. The proposed model can capture long term dependencies in stock price data in order to enhance the prediction accuracy. The accuracy of the proposed model is evaluated on five Stock Exchange of Thailand (SET) stocks, between 5 January 2015 and 29 December 2017, and compared the results with support vector Machine, multilayer perceptron, decision tree, random forest, logistic regression and k-nearest neighbors. The experimental results signify that the proposed model can outperform all comparative models.
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Determination of factors influencing the decision on purchasing organic food
(2017-01-01)
Atthirawong, Walailak
Since there has been a rising awareness about health, food safety and environmental issues, the demand for organic food has grown rapidly among consumers. However, information and profiling of consumers in Bangkok, which is the capital of Thailand, are not yet well reported. As such, this study aims at analyzing factors affecting consumers' organic food purchase intention. Cross-sectional data were carried out with the respondents in Bangkok through questionnaires. Data were analyzed using a logistic regression model employed to test the proposed hypotheses. The results revealed that education level and attitude towards place had positively influenced consumer's decision in buying organic food. Although the respondents had high score on attitude about healthfulness and food safety towards organic food, it was found that they had little knowledge about them. Evidence provided in this study could be employed as a reference information for policy makers and marketers regarding such issue.

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