Use of Word Clustering to Improve Emotion Recognition from Short Text

Abstract

Emotion recognition is an important component of affective computing, and is significant in the implementation of natural and friendly human-computer interaction. An effective approach to recognizing emotion from text is based on a machine learning technique, which deals with emotion recognition as a classification problem. However, in emotion recognition, the texts involved are usually very short, leaving a very large, sparse feature space, which decreases the performance of emotion classification. This paper proposes to resolve the problem of feature sparseness, and largely improve the emotion recognition performance from short texts by doing the following: representing short texts with word cluster features, offering a novel word clustering algorithm, and using a new feature weighting scheme. Emotion classification experiments were performed with different features and weighting schemes on a publicly available dataset. The experimental results suggest that the word cluster features and the proposed weighting scheme can partly resolve problems with feature sparseness and emotion recognition performance. Category: Smart and intelligent computing

Emotional sentiment analysis of social media content for mental health safety

“…In this paper, we employed supervised and unsupervised classifiers using machine learning. With regards to sentiment and emotion recognition from text, we take for example Yuan and Huang [47] in their paper they look into sentence-level categorization, as well as review-level categorization, their research and analysis make use of the Scikitlearn program which is a Python-based accessible software library which they use unsupervised techniques to measure similarity. On the other hand, IBM API analysis and similarity calculation using the Euclidian distance performed better than other machine learning methods and eventually outperforms the Scikit-learn program.…”