A lightweight clustering–based approach to discover different emotional shades from social message streams

Abstract

With the explosion of social media, automatic analysis of sentiment and emotion from user‐generated content has attracted the attention of many research areas and commercial‐marketing domains targeted at studying the social behavior of web users and their public attitudes toward brands, social events, and political actions. Capturing the emotions expressed in the written language could be crucial to support the decision‐making processes: the emotion resulting from a tweet or a review about an item could affect the way to advertise or to trade on the web and then to make predictions about future changes in popularity or market behavior. This paper presents an experience with the emotion‐based classification of textual data from a social network by using an extended version of the fuzzy C‐means algorithm called extension of fuzzy C‐means. The algorithm shows interesting results due to its intrinsic fuzzy nature that reflects the human feeling expressed in the text, often composed of a mix of blurred emotions, and at the same time, the benefits of the extended version yield better classification results.

Improving the emotion‐based classification by exploiting the fuzzy entropy in FCM clustering

“…The approach presented in Reference [7] achieves an emotion‐based classification of social data streams. It uses an extended version of the well‐known FCM algorithm, called EFCM 8 …”

“…Empirical evidence shows that the introduction of fuzzy entropy as a variation of the EFCM algorithm enhances the performance of the proposed classifier in terms of accuracy and runtime, compared with the previous approach 7 …”

“…This paper proposes Fuzzy Entropy Light Emotion Classification (FELEC), a novel framework for the classification of message streams, achieved by defining categories representing Plutchik's emotions. It is based on the work presented in Reference [7] but exploits a weighted variation of FCM, called entropy weighted fuzzy c‐means (EwFCM), 9 in substitution of the EFCM algorithm proposed in the previous approach. EwFCM uses the Fuzzy Entropy measures 10 to optimize the selection of initial cluster centers, which, due to the random cluster initialization, represents a known drawback of the traditional FCM method.…”

Learning to disentangle emotion factors for facial expression recognition in the wild

“…Automatic analysis of emotion has drawn attention of many research areas 1 . Facial expressions is a major nonverbal communication method for expressing emotions and intentions.…”

Kernel‐based low‐rank tensorized multiview spectral clustering

“…Besides, clustering has always been an essential task in data mining. It has a wide range of applications in many fields, such as medical diagnosis, 2 image processing, 3,4 grouping in crowd evaluation, 5,6 social network analysis, 7 time series analysis, 8 and so on. Aiming to partition data into different clusters with multiple views, multiview clustering is an important branch of clustering.…”