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

Abstract

Emotion detection in the natural language text has drawn the attention of several scientific communities as well as commercial/marketing companies: analyzing human feelings expressed in the opinions and feedback of web users helps understand general moods and support market strategies for product advertising and market predictions. This paper proposes a framework for emotion‐based classification from social streams, such as Twitter, according to Plutchik's wheel of emotions. An entropy‐based weighted version of the fuzzy c‐means (FCM) clustering algorithm, called EwFCM, to classify the data collected from streams has been proposed, improved by a fuzzy entropy method for the FCM center cluster initialization. Experimental results show that the proposed framework provides high accuracy in the classification of tweets according to Plutchik's primary emotions; moreover, the framework also allows the detection of secondary emotions, which, as defined by Plutchik, are the combination of the primary emotions. Finally, a comparative analysis with a similar fuzzy clustering‐based approach for emotion classification shows that EwFCM converges more quickly with better performance in terms of accuracy, precision, and runtime. Finally, a straightforward mapping between the computed clusters and the emotion‐based classes allows the assessment of the classification quality, reporting coherent and consistent results.

Clustering Algorithms in Sentiment Analysis Techniques in Social Media – A Rapid Literature Review

“… Clustering text [46]: These algorithms are a type of unsupervised learning algorithms that are used in SA to group similar text samples together based on their sentiment. Clustering algorithms can be useful for tasks such as discovering latent themes/topics [15] within a dataset of text or grouping similar text samples together for further analysis. In line with the above concepts, [9] use Latent Dirichlet Allocation (LDA) and K-means to extract themes among the topics dis-cussed on Twitter/X posts in relation to natural disaster.…”

“…In the light of the above, LDA is a generative probabilistic model of a corpus [54] used as a topic modeling algorithm that can discover the underlying themes in a collection of documents. LDA can automatically identify latent topics [15] in a set of reviews and offer a method to comprehend how various sentiments are distributed across a dataset.…”

“…SA, a component of NLP, is employed in SM and other online environments to analyze and understand the emotions, opinions, and attitudes expressed in text. This endeavor can be done through a variety of methods, including Machine learning (ML) [12], [13], [14], NLP [15] [16], and text analytics [17], [18]. To evaluate whether a text's overall sentiment is positive, negative, or neutral is the ultimate goal of SA.…”

Multihorizons transfer strategy for continuous online prediction of time‐series data in complex systems

“…The source domain incorporates data with multiple distribution characteristics. The feature classification [25][26][27] of the source domain data is the beginning of constructing different horizons. However, in this process, we do not need to assign corresponding labels to each type of data, but only need to group data with similar or the same characteristics into one category.…”

A Fuzzy-Based Emotion Detection Method to Classify the Relevance of Pleasant/Unpleasant Emotions Posted by Users in Reviews of Service Facilities

“…An extension of the Fuzzy C-means (FCM) partitive clustering algorithm is applied to classify documents where the term frequency inverse document frequency measure (TF-IDF) is used to assess the relevance of emotion categories in the document. A variation of this framework is used in [12] to classify document extracts from tweets; the authors applied an entropy variation of FCM to classify documents using the primary and secondary pleasant and unpleasant Plutchik emotion categories and they showed that their approach improves the classification accuracy of the emotion classification framework [11]. In [13], a new emotion classification framework called FREDoC is proposed; it is based on a fuzzy-based emotion classification model applied instead of the FCM clustering algorithm to classify documents considering the primary and secondary Plutchik emotion categories.…”