Special Issue "Emerging Application of Sentiment Analysis Technologies"

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

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 7215

Special Issue Editors

Prof. Dr. Miguel A. Alonso
E-Mail Website1 Website2
Guest Editor
CITIC. Grupo LyS, Departamento de Ciencias da Computación e Tecnoloxías da Información, Universidade da Coruña, 15071 A Coruña, Spain
Interests: natural language processing (NLP); multilingual and crosslingual NLP with an emphasis on low-resource languages; sentiment analysis and opinion mining on social media; information retrieval techniques applying NLP
Special Issues, Collections and Topics in MDPI journals
Prof. David Vilares
E-Mail Website
Guest Editor
Universidade da Coruña, CITIC. Grupo LyS, Departamento de Ciencias da Computación e Tecnoloxías da Información, 15071 A Coruña, Spain
Interests: natural language processing (NLP); parsing; NLP for low-resource languages; sentiment analysis and opinion mining; question answering

Special Issue Information

Dear Colleagues, 

Sentiment analysis (SA) is the field of natural language processing that deals with the automatic comprehension of opinions shared by users across different media. The traditional application of SA is to scan the opinions of users written on social networks and websites to find out how people perceive a product or service—which aspects are working and which have to be improved. This has caused a surge of technological solutions for SA, leading to the development of a wide range of systems, from those based on knowledge that makes use of lexical, syntactic and semantic information to those that are based on supervised learning through the creation of large pre-trained models of deep neural networks. These systems allow SA to be applied to areas that go beyond the product perceptions and written text.

Current challenges include analyzing texts in which segments written in one language are mixed with segments written in another language (code switching) or in which some elements are written in a language using morphological and syntactic structures from another language (code mixing); jointly analyzing the information provided by images and video together with the associated text (captions, subtitles, descriptions, etc.), so common in the web nowadays; extending sentiment analysis techniques so that they are not only able to determine people’s emotions but also their behavior, to aid in the prevention of sexism, racism, abuse and harassment on social networks; determining people’s mental health in order to alert serious conditions, such as depression and suicidal tendencies; to give just a few examples. In this Special Issue, we thus focus on approaches defining models and resources to deal with new challenging applications of SA technology in an effective manner.

Topic list

  • Multilingual sentiment analysis;
  • Sentiment analysis for low-resource languages;
  • Multimedia sentiment analysis from video, image and text;
  • Sentiment analysis for political, social and economic analysis;
  • Sentiment analysis for security monitoring;
  • Sentiment analysis for detecting sexism, racism, bullying and harassment;
  • Sentiment analysis for health;
  • Language resources for emerging applications of SA.

Technical Program Committee Members:

  1. Prof. Dr. Carlos Gómez-Rodríguez, Universidade da Coruña
  2. Prof. Dr. Jesús Vilares, Universidade da Coruña

Prof. Dr. Miguel A. Alonso
Prof. David Vilares
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.

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 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

  • Multilingual sentiment analysis
  • Sentiment analysis for low-resource languages
  • Multimedia sentiment analysis from video, image and text
  • Sentiment analysis for political, social and economic analysis
  • Sentiment analysis for security monitoring
  • Sentiment analysis for detecting sexism, bullying and harassment
  • Sentiment analysis for health
  • Language resources for emerging applications of SA

Published Papers (5 papers)

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Research

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Article
Multitasking Learning Model Based on Hierarchical Attention Network for Arabic Sentiment Analysis Classification
Electronics 2022, 11(8), 1193; https://doi.org/10.3390/electronics11081193 - 09 Apr 2022
Viewed by 489
Abstract
Limited approaches have been applied to Arabic sentiment analysis for a five-point classification problem. These approaches are based on single task learning with a handcrafted feature, which does not provide robust sentence representation. Recently, hierarchical attention networks have performed outstandingly well. However, when [...] Read more.
Limited approaches have been applied to Arabic sentiment analysis for a five-point classification problem. These approaches are based on single task learning with a handcrafted feature, which does not provide robust sentence representation. Recently, hierarchical attention networks have performed outstandingly well. However, when training such models as single-task learning, these models do not exhibit superior performance and robust latent feature representation in the case of a small amount of data, specifically on the Arabic language, which is considered a low-resource language. Moreover, these models are based on single task learning and do not consider the related tasks, such as ternary and binary tasks (cross-task transfer). Centered on these shortcomings, we regard five ternary tasks as relative. We propose a multitask learning model based on hierarchical attention network (MTLHAN) to learn the best sentence representation and model generalization, with shared word encoder and attention network across both tasks, by training three-polarity and five-polarity Arabic sentiment analysis tasks alternately and jointly. Experimental results showed outstanding performance of the proposed model, with high accuracy of 83.98%, 87.68%, and 84.59 on LABR, HARD, and BRAD datasets, respectively, and a minimum macro mean absolute error of 0.632% on the Arabic tweets dataset for five-point Arabic sentiment classification problem. Full article
(This article belongs to the Special Issue Emerging Application of Sentiment Analysis Technologies)
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Article
Mixing and Matching Emotion Frameworks: Investigating Cross-Framework Transfer Learning for Dutch Emotion Detection
Electronics 2021, 10(21), 2643; https://doi.org/10.3390/electronics10212643 - 29 Oct 2021
Cited by 1 | Viewed by 463
Abstract
Emotion detection has become a growing field of study, especially seeing its broad application potential. Research usually focuses on emotion classification, but performance tends to be rather low, especially when dealing with more advanced emotion categories that are tailored to specific tasks and [...] Read more.
Emotion detection has become a growing field of study, especially seeing its broad application potential. Research usually focuses on emotion classification, but performance tends to be rather low, especially when dealing with more advanced emotion categories that are tailored to specific tasks and domains. Therefore, we propose the use of the dimensional emotion representations valence, arousal and dominance (VAD), in an emotion regression task. Firstly, we hypothesize that they can improve performance of the classification task, and secondly, they might be used as a pivot mechanism to map towards any given emotion framework, which allows tailoring emotion frameworks to specific applications. In this paper, we examine three cross-framework transfer methodologies: multi-task learning, in which VAD regression and classification are learned simultaneously; meta-learning, where VAD regression and emotion classification are learned separately and predictions are jointly used as input for a meta-learner; and a pivot mechanism, which converts the predictions of the VAD model to emotion classes. We show that dimensional representations can indeed boost performance for emotion classification, especially in the meta-learning setting (up to 7% macro F1-score compared to regular emotion classification). The pivot method was not able to compete with the base model, but further inspection suggests that it could be efficient, provided that the VAD regression model is further improved. Full article
(This article belongs to the Special Issue Emerging Application of Sentiment Analysis Technologies)
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Article
Fine-Grained Implicit Sentiment in Financial News: Uncovering Hidden Bulls and Bears
Electronics 2021, 10(20), 2554; https://doi.org/10.3390/electronics10202554 - 19 Oct 2021
Viewed by 614
Abstract
The field of sentiment analysis is currently dominated by the detection of attitudes in lexically explicit texts such as user reviews and social media posts. In objective text genres such as economic news, indirect expressions of sentiment are common. Here, a positive or [...] Read more.
The field of sentiment analysis is currently dominated by the detection of attitudes in lexically explicit texts such as user reviews and social media posts. In objective text genres such as economic news, indirect expressions of sentiment are common. Here, a positive or negative attitude toward an entity must be inferred from connotational or real-world knowledge. To capture all expressions of subjectivity, a need exists for fine-grained resources and approaches for implicit sentiment analysis. We present the SENTiVENT corpus of English business news that contains token-level annotations for target spans, polar spans, and implicit polarity (positive, negative, or neutral investor sentiment, respectively). We both directly annotate polar expressions and induce them from existing schema-based event annotations to obtain event-implied implicit sentiment tuples. This results in a large dataset of 12,400 sentiment–target tuples in 288 fully annotated articles. We validate the created resource with an inter-annotator agreement study and a series of coarse- to fine-grained supervised deep-representation-learning experiments. Agreement scores show that our annotations are of substantial quality. The coarse-grained experiments involve classifying the positive, negative, and neutral polarity of known polar expressions and, in clause-based experiments, the detection of positive, negative, neutral, and no-polarity clauses. The gold coarse-grained experiments obtain decent performance (76% accuracy and 63% macro-F1) and clause-based detection shows decreased performance (65% accuracy and 57% macro-F1) with the confusion of neutral and no-polarity. The coarse-grained results demonstrate the feasibility of implicit polarity classification as operationalized in our dataset. In the fine-grained experiments, we apply the grid tagging scheme unified model for <polar span, target span, polarity> triplet extraction, which obtains state-of-the-art performance on explicit sentiment in user reviews. We observe a drop in performance on our implicit sentiment corpus compared to the explicit benchmark (22% vs. 76% F1). We find that the current models for explicit sentiment are not directly portable to our implicit task: the larger lexical variety within implicit opinion expressions causes lexical data scarcity. We identify common errors and discuss several recommendations for implicit fine-grained sentiment analysis. Data and source code are available. Full article
(This article belongs to the Special Issue Emerging Application of Sentiment Analysis Technologies)
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Review

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Review
Artificial Intelligence, Social Media and Supply Chain Management: The Way Forward
Electronics 2021, 10(19), 2348; https://doi.org/10.3390/electronics10192348 - 25 Sep 2021
Viewed by 1281
Abstract
Supply chain management (SCM) is a complex network of multiple entities ranging from business partners to end consumers. These stakeholders frequently use social media platforms, such as Twitter and Facebook, to voice their opinions and concerns. AI-based applications, such as sentiment analysis, allow [...] Read more.
Supply chain management (SCM) is a complex network of multiple entities ranging from business partners to end consumers. These stakeholders frequently use social media platforms, such as Twitter and Facebook, to voice their opinions and concerns. AI-based applications, such as sentiment analysis, allow us to extract relevant information from these deliberations. We argue that the context-specific application of AI, compared to generic approaches, is more efficient in retrieving meaningful insights from social media data for SCM. We present a conceptual overview of prevalent techniques and available resources for information extraction. Subsequently, we have identified specific areas of SCM where context-aware sentiment analysis can enhance the overall efficiency. Full article
(This article belongs to the Special Issue Emerging Application of Sentiment Analysis Technologies)
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Review
Sentiment Analysis for Fake News Detection
Electronics 2021, 10(11), 1348; https://doi.org/10.3390/electronics10111348 - 05 Jun 2021
Cited by 6 | Viewed by 2616
Abstract
In recent years, we have witnessed a rise in fake news, i.e., provably false pieces of information created with the intention of deception. The dissemination of this type of news poses a serious threat to cohesion and social well-being, since it fosters political [...] Read more.
In recent years, we have witnessed a rise in fake news, i.e., provably false pieces of information created with the intention of deception. The dissemination of this type of news poses a serious threat to cohesion and social well-being, since it fosters political polarization and the distrust of people with respect to their leaders. The huge amount of news that is disseminated through social media makes manual verification unfeasible, which has promoted the design and implementation of automatic systems for fake news detection. The creators of fake news use various stylistic tricks to promote the success of their creations, with one of them being to excite the sentiments of the recipients. This has led to sentiment analysis, the part of text analytics in charge of determining the polarity and strength of sentiments expressed in a text, to be used in fake news detection approaches, either as a basis of the system or as a complementary element. In this article, we study the different uses of sentiment analysis in the detection of fake news, with a discussion of the most relevant elements and shortcomings, and the requirements that should be met in the near future, such as multilingualism, explainability, mitigation of biases, or treatment of multimedia elements. Full article
(This article belongs to the Special Issue Emerging Application of Sentiment Analysis Technologies)
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