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Emotion detection and Sentiment analysis techniques are used to understand polarity or emotions expressed by people in many cases, especially during interactive systems use. Recognizing users' emotions is an important topic for human–computer interaction. Computers that recognize emotions would provide more natural interactions. Also, emotion detection helps design human-centred systems that provide adaptable behaviour change interventions based on users' emotions. The growing capability of machine learning to analyze big data and extract emotions therein has led to a surge in research in this domain. With this increased attention, it becomes essential to investigate this research area and provide a comprehensive review of the current state. In this paper, we conduct a systematic review of 123 papers on machine learning-based emotion detection to investigate research trends along many themes, including machine learning approaches, application domain, data, evaluation, and outcome. The results demonstrate: 1) increasing interest in this domain, 2) supervised machine learning (namely, SVM and Naïve Bayes) are the most popular algorithms, 3) Text datasets in the English language are the most common data source, and 4) most research use Accuracy to evaluate performance. Based on the findings, we suggest future directions and recommendations for developing human-centred systems. [ABSTRACT FROM AUTHOR]

The ability to exploit students' sentiments using different machine learning techniques is considered an important strategy for planning and manoeuvring in a collaborative educational environment. The advancement of machine learning technology is energised by the healthy growth of big data technologies. This helps the applications based on Sentiment Mining (SM) using big data to become a common platform for data mining activities. However, very little has been studied on the sentiment application using a huge amount of available educational data. Therefore, this paper has made an attempt to mine the academic data using different efficient machine learning algorithms. The contribution of this paper is two-fold: (i) studying the sentiment polarity (positive, negative and neutral) from students' data using machine learning techniques, and (ii) modelling and predicting students' emotions (Amused, Anxiety, Bored, Confused, Enthused, Excited, Frustrated, etc.) using the big data frameworks. The developed SM techniques using big data frameworks can be scaled and made adaptable for source variation, velocity and veracity to maximise value mining for the benefit of students, faculties and other stakeholders. [ABSTRACT FROM AUTHOR]

Emotion is a cognitive process and is one of the important characteristics of human beings that makes them different from machines. Traditionally, interactions between humans and machines like computers do not exhibit any emotional exchanges. If we could build any system that is intelligent enough to interact with humans that involves emotions, that is, it can detect user emotions and change its behaviour accordingly, then using machines could be more effective and friendly. Many approaches have been taken to detect user emotions. Affective computing is the field that detects user emotion in a particular moment. Our approach in this paper is to detect user emotions by analysing the keyboard typing patterns of the user and the type of texts (words, sentences) typed by them. This combined analysis gives us a promising result showing a substantial number of emotional states detected from user input. Several machine learning algorithms were used to analyse keystroke timing attributes and text pattern. We have chosen keystroke because it is the cheapest and most available medium to interact with computers. We have considered seven emotional classes for classifying the emotional states. For text pattern analysis, we have used vector space model with Jaccard similarity method to classify free-text input. Our combined approach showed above 80% accuracies in identifying emotions. [ABSTRACT FROM PUBLISHER]