Mining sentiments in SMS texts for teaching evaluation

Leong, Chee Kian; Lee, Yew Haur; Mak, Wai Keong

doi:10.1016/j.eswa.2011.08.113

Cited by 96 publications

(48 citation statements)

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“…However, the automatic processing of micro-blogging posts can be problematic because of the use of non-standard words and unusual punctuation [9]. Leong, et al [10] use sentiment mining to analyze SMS messages in teaching evaluations.…”

Section: Introductionmentioning

confidence: 99%

Smartphone Message Sentiment Analysis

Andriotis

Takasu

Tryfonas

2014

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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Humans tend to use specific words to express their emotional states in written and oral communications. Scientists in the area of text mining and natural language processing have studied sentiment fingerprints residing in text to extract the emotional polarity of customers for a product or to evaluate the popularity of politicians. Recent research focused on micro-blogging has found notable similarities between Twitter feeds and SMS (short message service) text messages. This paper investigates the common characteristics of both formats for sentiment analysis purposes and verifies the correctness of the similarity assumption. A lexicon-based approach is used to extract and compute the sentiment scores of SMS messages found on smartphones. The data is presented along a timeline that depicts a sender's emotional fingerprint. This form of analysis and visualization can enrich a forensic investigation by conveying potential psychological patterns from text messages.

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Section: Introductionmentioning

confidence: 99%

Smartphone Message Sentiment Analysis

Andriotis

Takasu

Tryfonas

2014

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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“…The text mining applications using these resources are usually related to text classification, mainly focusing on sentiment analysis. In this context, sentiment analysis has been applied in order to solve different problems, such as: to extract opinion about the educational environment (Kechaou, Ammar, & Alimi, ), to assist the instructor to improve the educational environment increasing the student engagement and create an adaptive educational environment (Ortigosa, Martín, & Carro, ), and to aid in teaching evaluation and provide feedback based on the user interaction on social network (Leong, Lee, & Mak, ). Different features can be applied in order to extract sentiment from online educational platforms by using traditional statistical features like TFIDF, Information Gain, Mutual Information and CHI statistics to natural language processing ones (Kechaou et al, ; Truong, ).…”

Section: Educational Sources and Resourcesmentioning

confidence: 99%

Text mining in education

Mello

Andre

Pinheiro

et al. 2019

WIREs Data Min & Knowl

143

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The explosive growth of online education environments is generating a massive volume of data, specially in text format from forums, chats, social networks, assessments, essays, among others. It produces exciting challenges on how to mine text data in order to find useful knowledge for educational stakeholders. Despite the increasing number of educational applications of text mining published recently, we have not found any paper surveying them. In this line, this work presents a systematic overview of the current status of the Educational Text Mining field. Our final goal is to answer three main research questions: Which are the text mining techniques most used in educational environments? Which are the most used educational resources? And which are the main applications or educational goals? Finally, we outline the conclusions and the more interesting future trends. This article is categorized under: Application Areas > Education and Learning Ensemble Methods > Text Mining

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“…Another area of EDM application is on student assessment and evaluation, which enables student proficiency to be distinguished at a fine-grained level (Lopez et al, 2012). EDM also facilitates student feedback and support (Leong et al, 2012). More generally, EDM can be applied to educational problems with regards to emotion in context, engagement, meta-cognition, and collaboration tasks (Baker, 2014).…”

Section: 1mentioning

confidence: 99%

A multi-objective evolutionary algorithm-based soft computing model for educational data mining

Tan

Lim

Bong

et al. 2017

AAOUJ

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For AuthorsIf you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation.*Related content and download information correct at time of download. Purpose -The purpose of this paper is to propose a soft computing model based on multi-objective evolutionary algorithm (MOEA), namely, modified micro genetic algorithm (MmGA) coupled with a decision tree (DT)-based classifier, in classifying and optimising the students' online interaction activities as classifier of student achievement. Subsequently, the results are transformed into useful information that may help educator in designing better learning instructions geared towards higher student achievement. Design/methodology/approach -A soft computing model based on MOEA is proposed. It is tested on benchmark data pertaining to student activities and achievement obtained from the University of California at Irvine machine learning repository. Additional, a real-world case study in a distance learning institution, namely, Wawasan Open University in Malaysia has been conducted. The case study involves a total of 46 courses collected over 24 consecutive weeks with students across the entire regions in Malaysia and worldwide.Findings -The proposed model obtains high classification accuracy rates at reduced number of features used. These results are transformed into useful information for the educational institution in our case study in an effort to improve student achievement. Whether benchmark or real-world case study, the proposed model successfully reduced the number features used by at least 48 per cent while achieving higher classification accuracy. Originality/value -A soft computing model based on MOEA, namely, MmGA coupled with a DT-based classifier, in handling educational data is proposed.

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Mining sentiments in SMS texts for teaching evaluation

Cited by 96 publications

References 1 publication

Smartphone Message Sentiment Analysis

Smartphone Message Sentiment Analysis

Text mining in education

A multi-objective evolutionary algorithm-based soft computing model for educational data mining

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