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Electronics
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Sentiment Analysis and eWOM Advancements with Deep Learning and Artificial...
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Sentiment Analysis and eWOM Advancements with Deep Learning and Artificial Intelligence

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (31 December 2024) | Viewed by 1846

Special Issue Editors

Dr. Ramón Carrasco

E-Mail Website
Guest Editor
Department of Marketing, Faculty of Statistics, Complutense University of Madrid, 28040 Madrid, Spain
Interests: multi-criteria decision-making models; sentiment analysis; recommender systems; data science applied to business, machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Francisco Mata

E-Mail Website
Guest Editor
Department of Computer Science, University of Jaén, 23071 Jaén, Spain
Interests: group decision making; consensus reaching processes; smart cities and citizen participation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The purpose of this Special Issue is to delve deeply into sentiment analysis through the use of deep learning and artificial intelligence techniques in the context of consumer interactions on social media.

In the digital age, the exchange of opinions and reviews on online platforms has grown exponentially, leading to the emergence of electronic word of mouth (e-WOM), which significantly impacts users' purchasing decisions and their perceptions of products and services.

This Special Issue aims to explore how sentiment analysis can help companies to better understand consumer needs and preferences, identify areas for improvement in their products or services, and effectively segment their online audience. Artificial intelligence techniques such as deep learning and transformer models, which are essential for analyzing large volumes of data generated by users in natural language, will be explored. In summary, this Special Issue aspires to offer a comprehensive and updated insight into the impact and applications of sentiment analysis in the realm of e-WOM and business decision-making.

Dr. Ramón Carrasco
Dr. Francisco Mata
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • sentiment analysis
  • electronic word of mouth
  • artificial intelligence
  • deep learning
  • natural language processing

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Published Papers (2 papers)

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Research

19 pages, 1164 KiB  
Article
Integrating Technical Analysis into Sentiment Analysis: An ASTE Framework for Electric Car Purchase Decision Support Based on LLMs and Semantic BNF
by Álvaro Carrasco-Aguilar, M. Mercedes Carmona-Martínez, María C. Parra-Meroño and Mar Souto-Romero
Electronics 2025, 14(5), 1020; https://doi.org/10.3390/electronics14051020 - 4 Mar 2025
Viewed by 493
Abstract
The increasing complexity of purchasing an electric car, influenced by technical specifications and expert reviews, requires advanced Natural Language Processing techniques to extract meaningful insights. This study enhances Aspect Sentiment Triplet Extraction (ASTE) by integrating Large Language Models (LLMs) to identify key aspects, [...] Read more.
The increasing complexity of purchasing an electric car, influenced by technical specifications and expert reviews, requires advanced Natural Language Processing techniques to extract meaningful insights. This study enhances Aspect Sentiment Triplet Extraction (ASTE) by integrating Large Language Models (LLMs) to identify key aspects, opinions, and sentiments in expert reviews, including technical data traditionally classified as neutral, such as horsepower and battery range. A semantic extension of Backus–Naur Form (BNF) structures input queries for syntactic and semantic accuracy, while a 2-tuple fuzzy linguistic model refines sentiment representation, ensuring interpretability. The proposed model addresses limitations in existing ASTE techniques by incorporating formal grammar structures and linguistic modeling, eliminating the need for complex preprocessing. Applied to expert YouTube reviews of electric cars, the method leverages Google’s Gemini model via Python and the Gemini API to rank the top-selling electric cars in the United States. The results confirm the model’s effectiveness in aligning technical data with sentiment analysis, making it accessible to non-specialists in Natural Language Processing. This framework enhances decision support in electric car purchases by providing a structured, interpretable, and contextually rich sentiment analysis approach. Full article
(This article belongs to the Special Issue Sentiment Analysis and eWOM Advancements with Deep Learning and Artificial Intelligence)
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25 pages, 3595 KiB  
Article
Customer Electronic Word of Mouth Management Strategies Based on Computing with Words: The Case of Spanish Luxury Hotel Reviews on TripAdvisor
by Ziwei Shu, Miguel Llorens-Marin, Ramón Alberto Carrasco and Mar Souto Romero
Electronics 2025, 14(2), 325; https://doi.org/10.3390/electronics14020325 - 15 Jan 2025
Viewed by 1031
Abstract
The rapid growth of the internet and social media has made electronic word of mouth (eWOM) a key element of modern marketing. In the hospitality industry, nowadays, effective eWOM management is essential for developing impactful strategies and fostering customer satisfaction. This paper introduces [...] Read more.
The rapid growth of the internet and social media has made electronic word of mouth (eWOM) a key element of modern marketing. In the hospitality industry, nowadays, effective eWOM management is essential for developing impactful strategies and fostering customer satisfaction. This paper introduces an enhanced approach to strategic customer base management based on online reviews by extending the Recency, Frequency, and Monetary (RFM) model with three novel dimensions, the Helpfulness, Promoter Score, and Stability of the customer, thereby forming the RFHPS model. It also includes the 2-tuple linguistic model, one of the most popular computing with words models, to improve precision in the RFHPS score’s computation and the findings’ interpretability. Using K-means clustering, customers are segmented across these five dimensions. The data on luxury hotels in Spain gathered from TripAdvisor demonstrate the model’s applicability. By integrating this framework into customer relationship management systems, managers can tailor marketing strategies for distinct segments, facilitating deeper customer understanding and bolstering eWOM generation. Full article
(This article belongs to the Special Issue Sentiment Analysis and eWOM Advancements with Deep Learning and Artificial Intelligence)
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