Sentiment analysis of preschool teachers’ perceptions on ICT use for young children
01 Dec 2019-pp 1-6
TL;DR: This paper summarizes the findings using sentiment analysis as well as comparing it to the quantitative data obtained from the survey, where most teachers agreed upon the benefits of ICT use and conclude more positive sentiment polarity.
Abstract: Sentiment analysis in gaining more attention as it is increasingly used in multiple domains, including in interpreting educational data. The article uses sentiment analysis technique to understand the early childhood educators reported beliefs (perception) on young children’s ICT use. The dataset was obtained from a comparative study of early childhood educators from two countries, Australia and Malaysia. The result shows a similar outcome where most teachers agreed upon the benefits of ICT use and conclude more positive sentiment polarity.This paper summarizes the findings using sentiment analysis as well as comparing it to the quantitative data obtained from the survey.
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Journal Article•
TL;DR: In this paper, a new architecture for opinion mining is proposed, which uses a multidimensional model to integrate customers' characteristics and their comments about products (or services) and transfer comments (opinions) to a fact table that includes several dimensions, such as, customers, products, time and locations.
Abstract: As e-commerce is becoming more and more popular, the number of customer reviews that a product receives grows rapidly. In order to enhance customer satisfaction and their shopping
experiences, it has become important to analysis customers reviews to extract opinions on the products that they buy. Thus, Opinion Mining is getting more important than before especially in doing analysis and forecasting about customers’ behavior for businesses purpose. The right decision in producing new products or services based on data about customers’ characteristics means profit for organization/company. This paper proposes a new architecture for Opinion Mining, which uses a multidimensional model to integrate customers’ characteristics and their comments about products (or services). The key step to achieve this objective is to transfer comments (opinions) to a fact table that includes several dimensions, such as, customers, products, time and locations. This research presents a comprehensive way to calculate customers’ orientation for all possible products’
attributes.
4 citations
10 Mar 2021
TL;DR: In this article, the authors highlight the importance of opinion analysis in the field of education in the Big Data era and highlight the need to take account of all facets of primary school education.
Abstract: The primary education sector is facing a significant transition to keep up to expectations in today's dynamic environment. Any part of primary school requires positive improvements. The paper highlights the importance of Opinion Analysis in the field of education in the Big Data era. With the generation of bulk data in recent years, innovations have been developed that make data collection and processing even simpler. A vast variety of data sets can be compiled, mined and made usable for sentiment analysis. Various target users, including the tutor, the pupil and the educational institution, have been discussed. The embodiment of human capital, tools and strategies , the education sector should make effective use of Sentiment Analysis to take account of all facets of primary school education. Thus, various algorithm are Naive Bayes, Complement Naive Bayes (CNB).
2 citations
Peer Review•
TL;DR: The majority of students have a positive opinion about elearning systems in Malaysian universities, and sentiment analysis-based Machine Learning (ML) with Support Vector Machine (SVM) was utilized, providing evidence for the effective use of sentiment analysis as an indicator that may contribute to the development of educational systems.
Abstract: Aim/Purpose To gain insight into the opinions and reviews of Malaysian university students regarding e-learning systems, thereby improving the quality and services of these systems and resolving any problems, concerns, and issues that may exist within the institution. Background This exploratory study examines the students’ perceptions of e-learning in Malaysia based on Sentiment Analysis (SA) to gain a clear insight into their feelings about the quality of e-learning systems and related services in Malaysian universities to determine whether these opinions are positive or negative. Methodology The data was collected from Twitter; the Full Archive Search API Premium v1.1 tire was chosen to access the tweets from November 1, 2019, to December 30, 2020. The R programming language library package “rtweet” was applied to access the search API and query the tweets. To classify students’ opinions, sentiment analysis-based Machine Learning (ML) with Support Vector Machine (SVM) was utilized. Rapid Miner, a statistical and data mining tool, was used to determine the sentiment of tweets and the accuracy of the ML algorithm. After preparing the data, RapidMiner was used to pre-process and classify the final 1201 tweets based on sentiment, and National Research Council (NRC) wordStudents’ Perceptions of E-Learning in Malaysian Universities 440 emotion lexicon was used to detect the presence of eight emotions in the tweets. The confusion matrix is used to determine the classifier’s performance. Contribution This research provided evidence for the effective use of sentiment analysis as an indicator that may contribute to the development of educational systems, specifically, e-learning systems in Malaysian universities. Findings Based on the findings, the majority of students have a positive opinion about elearning systems in Malaysian universities. Precisely, the results showed that 65% of sentiments were classified as positive and 35% as negative. Moreover, among the eight emotions, the majority of the tweets expressed a higher level of trust, anticipation, and joy. Recommendations for Practitioners The study findings could help classify the teachers’ strengths and weaknesses graphically based on the students’ positive and negative feedback. These findings would also help decision-makers and educationalists be more aware of students’ feelings (sentiments) and concerns. Thus, using social media sentiment analysis should be encouraged as a valuable source of information that may assist their educational decision-making, e-learning development, and performance evaluation. Recommendations for Researchers The findings may encourage other researchers to apply SA based ML approach and use Twitter as a data source to discover users’ opinions about certain issues in learning and teaching processes. Impact on Society Our study confirmed that social media data could provide valuable and supportive information about educational systems and procedures in e-learning for appropriate decision-making regarding future development and strategies. Future Research Future work can experiment with other classification models and different ML classification algorithms as well as other feature extraction methods and compare the results to find the best accuracy that can improve the classification results
References
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TL;DR: This survey paper tackles a comprehensive overview of the last update in this field of sentiment analysis with sophisticated categorizations of a large number of recent articles and the illustration of the recent trend of research in the sentiment analysis and its related areas.
2,152 citations
"Sentiment analysis of preschool tea..." refers background in this paper
...A label is assigned by the feature vector that has the highest probability for it [13], [16]....
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...one of the limitations for SVM [13], [16], [21]....
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...The supervised learning approach relies on the existence of labeled training dataset [16]....
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29 Apr 2012
TL;DR: This paper argues for increased and formal communication and collaboration between these communities in order to share research, methods, and tools for data mining and analysis in the service of developing both LAK and EDM fields.
Abstract: Growing interest in data and analytics in education, teaching, and learning raises the priority for increased, high-quality research into the models, methods, technologies, and impact of analytics. Two research communities -- Educational Data Mining (EDM) and Learning Analytics and Knowledge (LAK) have developed separately to address this need. This paper argues for increased and formal communication and collaboration between these communities in order to share research, methods, and tools for data mining and analysis in the service of developing both LAK and EDM fields.
801 citations
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...tools for data mining and analysis [2]....
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TL;DR: A new method for sentiment analysis in Facebook is presented, starting from messages written by users, to extract information about the users' sentiment polarity (positive, neutral or negative), as transmitted in the messages they write, and to model the Users' usual sentiment pol parity and to detect significant emotional changes.
508 citations
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...In the context of e-learning, [12] recommended the use of a hybrid approach to get the best accuracy....
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30 Apr 2017
166 citations
"Sentiment analysis of preschool tea..." refers methods in this paper
...” [3] Hence, the article aimed to re-introduce this artificial intelligence tool, by deploying sentiment analysis as another data analytical means....
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TL;DR: A comparison of results obtained by applying Naive Bayes and Support Vector Machine (SVM) classification algorithm to classify a sentimental review having either a positive review or negative review is presented.
108 citations
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...studies [19]–[21]....
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