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Grant support

This research work is partially supported by the Spanish Ministry of Economy, Spain through the project EmoSpaces (RTC-2016-5053-7) and the European Union with Trivalent (H2020 Action Grant No. 740934, SEC-06-FCT-2016), and the project Somedi (ITEA 15011).

Analysis of institutional authors

Araque, OscarCorresponding AuthorZhu, GanggaoAuthorIglesias, Carlos A.Author

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Article

A semantic similarity-based perspective of affect lexicons for sentiment analysis

Publicated to:Knowledge-Based Systems. 165 346-359 - 2019-02-01 165(), DOI: 10.1016/j.knosys.2018.12.005

Authors: Araque, O; Zhu, GG; Iglesias, CA

Affiliations

Univ Politecn Madrid, Intelligent Syst Grp, Ave Complutense 30, Madrid, Spain - Author

Abstract

Lexical resources are widely popular in the field of Sentiment Analysis, as they represent a resource that directly encodes sentimental knowledge. Usually sentiment lexica are used for polarity estimation through the matching of words contained in a text and their associated lexicon sentiment polarities. Nevertheless, such resources have limitations in vocabulary coverage and domain adaptation. Besides, many recent techniques exploit the concept of distributed semantics, normally through word embeddings. In this work, a semantic similarity metric is computed between text words and lexica vocabulary. Using this metric, this paper proposes a sentiment classification model that uses the semantic similarity measure in combination with embedding representations. In order to assess the effectiveness of this model, we perform an extensive evaluation. Experiments show that the proposed method can improve Sentiment Analysis performance over a strong baseline, being this improvement statistically significant. Finally, some characteristics of the proposed technique are studied, showing that the selection of lexicon words has an effect in cross-dataset performance. (C) 2018 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license.

Keywords

KnowledgeModelsSalesSemantic similaritySentiment analysisSentiment lexiconWord embeddingsWordnet

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Knowledge-Based Systems due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2019, it was in position 15/137, thus managing to position itself as a Q1 (Primer Cuartil), in the category Computer Science, Artificial Intelligence.

From a relative perspective, and based on the normalized impact indicator calculated from World Citations provided by WoS (ESI, Clarivate), it yields a value for the citation normalization relative to the expected citation rate of: 2.64. This indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: ESI Nov 14, 2024)

This information is reinforced by other indicators of the same type, which, although dynamic over time and dependent on the set of average global citations at the time of their calculation, consistently position the work at some point among the top 50% most cited in its field:

  • Weighted Average of Normalized Impact by the Scopus agency: 5.13 (source consulted: FECYT Feb 2024)
  • Field Citation Ratio (FCR) from Dimensions: 25.56 (source consulted: Dimensions Jun 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-06-24, the following number of citations:

  • WoS: 72
  • Scopus: 120
  • Google Scholar: 164
  • OpenCitations: 88

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-06-24:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 235.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 234 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 0.25.
  • The number of mentions on the social network X (formerly Twitter): 1 (Altmetric).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.

Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (ARAQUE IBORRA, OSCAR) and Last Author (IGLESIAS FERNANDEZ, CARLOS ANGEL).

the author responsible for correspondence tasks has been ARAQUE IBORRA, OSCAR.