scholarly journals A new significant area: Emotion detection in E-learning using opinion mining techniques

2009 ◽  
Author(s):  
H.H. Binali ◽  
Chen Wu ◽  
V. Potdar
Keyword(s):  
Opinion Mining ◽  
Emotion Detection ◽  
Significant Area ◽  
E Learning

scholarly journals E-Learner's Academic Emotions Based on Facial Expressions : A Survey

2020 ◽  
pp. 265-273
Author(s):  
Snehal Rathi ◽  
Arnav Sakhariya ◽  
Jeet Shah ◽  
Mohit Sanghvi
Keyword(s):  
Facial Expressions ◽  
Affective Domain ◽  
Vital Role ◽  
Emotion Detection ◽  
Academic Emotions ◽  
Face To Face ◽  
E Learning ◽  
Face To Face Learning ◽  
Academic Emotion ◽  
Day By Day

This paper presents different technologies and framework used for academic emotion detection using facial recognition in E-Learning. E-Learning is growing day by day for various reasons like distance learning and user is able to do it at anytime and anywhere. But E-Learning lacks in real time feedback from the students to teachers and vice-versa. Academic Emotion plays an important role on detecting whether the students have understood the topic or not. In face to face learning, a skilled teacher achieves affective domain goals by interacting with the students and asking them questions. But in online learning student and teacher are apart so if system itself finds the emotion and take the action accordingly, is really very helpful to teacher and student both. There are various ways like sensors, facial expressions, log usage are used by many scientists to achieve this. We have researched and read many papers about various frameworks used and found that academic emotions play a vital role and also makes big difference in learning if it is properly analyzed and suitable action is taken. A model for the same purpose has been proposed here which will detect emotion and generate feedback accordingly.

scholarly journals Aspect Based Sentiment Analysis for E-Commerce Shopping Website

2021 ◽  
Vol 9 (11) ◽  
pp. 1819-1822
Author(s):  
Neha V. Thakare
Keyword(s):  
Sentiment Analysis ◽  
Opinion Mining ◽  
Emotion Detection ◽  
Research Knowledge ◽  
Text Data ◽  
Customer Expectations ◽  
Customer Reviews ◽  
Classification Feature ◽  
On Line ◽  
Area Unit

Abstract: Sentiment Analysis is that the most ordinarily used approach to research knowledge that is within the form of text and to identify sentiment content from the text. Opinion Mining is another name for sentiment analysis. a good vary of text data is getting generated within the form of suggestions, feedback, tweets, and comments. E-Commerce portals area unit generating tons of data. Every day within the form of customer reviews. Analyzing E-Commerce data can facilitate on-line retailers to grasp customer expectations, offer an improved searching expertise, and to extend sales. Sentiment Analysis can be used to identify positive, negative, and neutral information from the customer reviews. Researchers have developed a lot of techniques in Sentiment Analysis. Keywords: Sentiment analysis, Sentiment classification, Feature selection, Emotion detection, Customer Reviews;

Studying the Suitability of Discourse Analysis Methods for Emotion Detection and Interpretation in Computer-Mediated Educational Discourse

2013 ◽  
pp. 119-143
Author(s):  
Thanasis Daradoumis ◽  
Marta María Arguedas Lafuente
Keyword(s):  
Discourse Analysis ◽  
Classroom Interaction ◽  
Emotion Detection ◽  
Learning Contexts ◽  
Analysis Methods ◽  
Educational Environments ◽  
Computer Mediated ◽  
Depth Study ◽  
E Learning ◽  
Regulation Of Emotion

Conversation analysis (CA) and discourse analysis (DA) methods have been widely used to analyse classroom interaction in conventional educational environments and to some extent in e-learning environments, paying more attention to the ’quality’ and purposes the discourse serves to accomplish in its specific context. However, CA and DA methods seem to ignore emotion detection and interpretation when analysing learners’ interaction in online environments. Effective regulation of emotion, motivation and cognition in social interaction has been shown to be crucial in achieving problem-solving goals. The aim of this chapter is to provide an in-depth study on the possibility of applying discourse analysis methods in e-learning contexts with implications for emotion detection, interpretation and regulation. The result of this study shows whether a comprehensive approach that includes DA methodological solutions and constructivist strategies (e.g., cognitive dissonance) for emotion detection and interpretation can be elaborated and applied.

scholarly journals DialogueRNN: An Attentive RNN for Emotion Detection in Conversations

2019 ◽  
Vol 33 ◽  
pp. 6818-6825 ◽  
Author(s):  
Navonil Majumder ◽  
Soujanya Poria ◽  
Devamanyu Hazarika ◽  
Rada Mihalcea ◽  
Alexander Gelbukh ◽  
...  
Keyword(s):  
Neural Networks ◽  
Social Media ◽  
Recurrent Neural Networks ◽  
Opinion Mining ◽  
State Of The Art ◽  
Emotion Detection ◽  
Emotion Classification ◽  
Argumentation Mining ◽  
Consumer Feedback ◽  
The Individual

Emotion detection in conversations is a necessary step for a number of applications, including opinion mining over chat history, social media threads, debates, argumentation mining, understanding consumer feedback in live conversations, and so on. Currently systems do not treat the parties in the conversation individually by adapting to the speaker of each utterance. In this paper, we describe a new method based on recurrent neural networks that keeps track of the individual party states throughout the conversation and uses this information for emotion classification. Our model outperforms the state-of-the-art by a significant margin on two different datasets.

scholarly journals A Learning Analytics Methodology for Detecting Sentiment in Student Fora: A Case Study in Distance Education

2015 ◽  
Vol 18 (2) ◽  
pp. 74-94 ◽  
Author(s):  
Vasileios Kagklis ◽  
Anthi Karatrantou ◽  
Maria Tantoula ◽  
Chris T. Panagiotakopoulos ◽  
Vassilios S. Verykios
Keyword(s):  
Opinion Mining ◽  
Educational Process ◽  
Key Factors ◽  
Common Goal ◽  
Postgraduate Students ◽  
E Learning ◽  
Overall Performance ◽  
Analyse Data ◽  
Patterns Of Interaction

Abstract Online fora have become not only one of the most popular communication tools in e-learning environments, but also one of the key factors of the learning process, especially in distance learning, as they can provide to the students involved, motivation for collaboration in order to achieve a common goal. The purpose of this study is to analyse data related to the participation of postgraduate students in the online forum of their course at the Hellenic Open University. The content of the messages posted is analysed by using text mining techniques, while the network through which the students interact is processed through social network analysis techniques. Furthermore, sentiment analysis and opinion mining is applied on the same dataset. Our aim is to study students’ attitude towards the course and its features, as well as to model their sentiment behaviour over time, and finally to detect if and how this affected their overall performance. The combined knowledge attained from the aforementioned techniques can provide tutors with practical and valuable information for the structure and the content of the students’ exchanged messages, the patterns of interaction among them, the trend of sentiment polarity during the course, so as to improve the educational process.

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