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Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: EmoSenticSpace: A novel framework for affective common-sense reasoning
Author(s): Poria, Soujanya
Gelbukh, Alexander
Cambria, Erik
Hussain, Amir
Huang, Guang-Bin
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Keywords: Sentic computing
opinion mining
sentiment analysis
emotion detection
personality detection
fuzzy clustering
Issue Date: Oct-2014
Date Deposited: 8-Jul-2014
Citation: Poria S, Gelbukh A, Cambria E, Hussain A & Huang G (2014) EmoSenticSpace: A novel framework for affective common-sense reasoning. Knowledge-Based Systems, 69, pp. 108-123.
Abstract: Emotions play a key role in natural language understanding and sensemaking. Pure machine learning usually fails to recognize and interpret emotions in text. The need for knowledge bases that give access to semantics and sentics (the conceptual and affective information) associated with natural language is growing exponentially in the context of big social data analysis. To this end, this paper proposes EmoSenticSpace, a new framework for affective common-sense reasoning that extends WordNet-Affect and SenticNet by providing both emotion labels and polarity scores for a large set of natural language concepts. The framework is built by means of fuzzy c-means clustering and support-vector-machine classification, and takes into account different similarity measures, such as point-wise mutual information and emotional affinity. EmoSenticSpace was tested on three emotion-related natural language processing tasks, namely sentiment analysis, emotion recognition, and personality detection. In all cases, the proposed framework outperforms the state of the art. In particular, the direct evaluation of EmoSenticSpace against the psychological features provided in the ISEAR dataset shows a 92.15% agreement.
DOI Link: 10.1016/j.knosys.2014.06.011
Rights: Published in Knowledge-Based Systems by Elsevier; Elsevier believes that individual authors should be able to distribute their AAMs for their personal voluntary needs and interests, e.g. posting to their websites or their institution’s repository, e-mailing to colleagues. However, our policies differ regarding the systematic aggregation or distribution of AAMs to ensure the sustainability of the journals to which AAMs are submitted. Therefore, deposit in, or posting to, subject-oriented or centralized repositories (such as PubMed Central), or institutional repositories with systematic posting mandates is permitted only under specific agreements between Elsevier and the repository, agency or institution, and only consistent with the publisher’s policies concerning such repositories. Voluntary posting of AAMs in the arXiv subject repository is permitted.

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