Abstract
Human Computer Interaction (HCI) researches the use of computer technology mainly focused on the interfaces between human users and computers. Expression of emotion comprises of challenging style as it is produced with plaint text and short messaging language as well. This research paper investigates on the overview of emotion recognition from various texts and expresses the emotion detection methodologies applying Machine Learning Approach (MLA). This paper recommends resolving the problem of feature meagerness, and largely improving the emotion recognition presentation from short texts by achieving the three aims: (I) The representing short texts along with word cluster features, (II) Presenting a narrative word clustering algorithm, and (iii) Making use of a new feature weighting scheme of the Emotion classification. Experiments were performed for the classifying the emotions with different features and weighting schemes, on the openly available dataset. We have used the word clusters in place of unigrams as features, the micro-averages of accuracy have been found to be enhanced by more than three percentage, which suggests that the overall accuracy value of the text emotion classifier has been improved. All the macro-averages were enhanced by more than one percentage, which suggests that the word cluster feature can advance the generalization potential of the emotion classifier. The experimental results suggest that the text words cluster features and the proposed weighting scheme can moderately resolve the problems of the emotion recognition performance and the feature sparseness.
Highlights
One of the important capabilities of the Intelligent machines is the ‘affective capability’, making them to understands and expresses emotions, has become an emerging research area in the domain of the artificial intelligence community which has been elaborated by Russell and Norvig in [01]
We proposed a narrative on emotion recognition approach based on the word clustering for short texts
In this paper we used the word clusters as features and proposed a weighting scheme based on the discrimination-based degree of word clusters as well as the representation degree of words
Summary
One of the important capabilities of the Intelligent machines is the ‘affective capability’, making them to understands and expresses emotions, has become an emerging research area in the domain of the artificial intelligence community which has been elaborated by Russell and Norvig in [01]. Cambria et al presented a thought called ‘affective computing technique’ and correlated the computations method that relates to the emotions [02]. Text messages are still the most popular communication medium at present. The text messages are having many applications and it is having important task to be recognized emotions from texts effectively. An intelligent conversation on tweeter can recognize emotions from a user’s discussion, it can give extra adaptive and human-like response. The recognizing emotions from text messages are noteworthy for the implementation of the appealing human-computer interaction
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
