Computational approaches for emotion detection in text

Abstract

Emotions are part and parcel of human life and among other things, highly influence decision making. Computers have been used for decision making for quite some time now but have traditionally relied on factual information.Recently, interest has been growing among researchers to find ways of detecting subjective information used in blogs and other online social media. This paper presents emotion theories that provide a basis for emotion models. It shows how these models have been used by discussing computational approaches to emotion detection. We propose a hybrid based architecture for emotion detection. The SVM algorithm is used for validating the proposed architecture and achieves a prediction accuracy of 96.43% on web blog data.

Predicting Emotional Reaction in Social Networks

“…. ) expressed in the content using computational models learned from labeled or distantly labeled sentiment or emotion corpora [1,4,7]. More recently work has also been done on the detection of emotion in a social network, but focusing on analyzing the emotion contained in text rather than its influence on others [5].…”

Ontology-Based Textual Emotion Detection

“… Haji Binali, Chen Wu, and Vidyasagar Potdar in [13]. They presented a hybrid based architecture comprising of keyword based component and learning system component.…”

Emotion recognition and its application in software engineering

“…-gestures: decision trees [41], Bayesian networks [8], [41]; -text: SVM [42], naïve Bayes, decision trees, k-nearest neighbours, neural networks [43]; -physiological signals: SVM, Bayesian networks, neural networks, linear logistic regression, naïve Bayes [43], k-nearest neighbours [44]; decision trees [45]; -keystroke dynamics: decision trees [36], Bayesian networks [46], neural networks [37]. Although some methods show higher accuracies than others, it is impossible to indicate the best methodology, because the reported results are incomparable.…”

“…A challenging problem of automatic recognition of human affect has become a research field involving more and more scientists specializing in different areas such as artificial intelligence, computer vision, psychology, physiology etc. A great many proposed algorithms differ mainly on information sources they use: -visual information processing [8], [9], -body movements analysis [10], [11], -text input lexical analysis [12], [13], -voice signals [10], [14], -standard input devices [15], [16], -physiological measurements [17].…”