Special Issue "Sentiment Analysis for Social Media Ⅱ"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 15 July 2021.

Special Issue Editors

Dr. Carlos A. Iglesias
Website
Guest Editor
Intelligent Systems Group, Universidad Politécnica de Madrid, 28040 Madrid, Spain
Interests: multiagent systems; social computing; linked data; natural language processing; affect technology; machine learning
Special Issues and Collections in MDPI journals
Dr. Antonio Moreno
Website
Guest Editor
Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, Tarragona, Spain
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Sentiment analysis is a research field that analyzes people’s opinions, stances, attitudes, and emotions from written text. The popularity of social media and social networks has fostered the quick development of this field, given its ability to analyze collective sentiments. 

This Special Issue “Sentiment Analysis for Social Media” aims to present new advances in the area of sentiment analysis that foster its development. Submissions are expected to focus on both the theoretical aspects and applications of sentiment analysis techniques for social media. New ideas proposing disruptive approaches are also welcome.

Topics of interest include but are not limited to the following areas:

  • Sentiment and emotion analysis in social media and social networks;
  • Evaluation of sentiment analysis systems;
  • Sentiment analysis and social science;
  • Visualization of sentiment analysis;
  • Big data systems for sentiment analysis.
  • Fairness and bias in sentiment analysis;
  • Language resources for sentiment analysis;
  • Semantic models for sentiment analysis;
  • Social network analysis for improving sentiment analysis;
  • Multimodal sentiment analysis;
  • Multilingual aspects of sentiment analysis;
  • Language resources and tools for sentiment analysis;
  • Applications of sentiment analysis;
  • Sentiment analysis in health applications;
  • Sentiment analysis and financial applications.

We hope this Special Issue works as a roadmap for all developers and users of sentiment analysis for getting insights from social media.

Dr. Carlos A. Iglesias
Dr. Antonio Moreno
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 papers will be 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. Applied Sciences 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 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.

Published Papers (6 papers)

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Research

Open AccessArticle
Introducing Sentiment Analysis of Textual Reviews in a Multi-Criteria Decision Aid System
Appl. Sci. 2021, 11(1), 216; https://doi.org/10.3390/app11010216 - 28 Dec 2020
Abstract
Nowadays, most decision processes rely not only on the preferences of the decision maker but also on the public opinions about the possible alternatives. The user preferences have been heavily taken into account in the multi-criteria decision making field. On the other hand, [...] Read more.
Nowadays, most decision processes rely not only on the preferences of the decision maker but also on the public opinions about the possible alternatives. The user preferences have been heavily taken into account in the multi-criteria decision making field. On the other hand, sentiment analysis is the field of natural language processing devoted to the development of systems that are capable of analysing reviews to obtain their polarity. However, there have not been many works up to now that integrate the results of this process with the analysis of the alternatives in a decision support system. SentiRank is a novel system that takes into account both the preferences of the decision maker and the public online reviews about the alternatives to be ranked. A new mechanism to integrate both aspects into the ranking process is proposed in this paper. The sentiments of the reviews with respect to different aspects are added to the decision support system as a set of additional criteria, and the ELECTRE methodology is used to rank the alternatives. The system has been implemented and tested with a restaurant data set. The experimental results confirm the appeal of adding the sentiment information from the reviews to the ranking process. Full article
(This article belongs to the Special Issue Sentiment Analysis for Social Media Ⅱ)
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Open AccessArticle
Personality Trait Analysis in Social Networks Based on Weakly Supervised Learning of Shared Images
Appl. Sci. 2020, 10(22), 8170; https://doi.org/10.3390/app10228170 - 18 Nov 2020
Abstract
Social networks have attracted the attention of psychologists, as the behavior of users can be used to assess personality traits, and to detect sentiments and critical mental situations such as depression or suicidal tendencies. Recently, the increasing amount of image uploads to social [...] Read more.
Social networks have attracted the attention of psychologists, as the behavior of users can be used to assess personality traits, and to detect sentiments and critical mental situations such as depression or suicidal tendencies. Recently, the increasing amount of image uploads to social networks has shifted the focus from text to image-based personality assessment. However, obtaining the ground-truth requires giving personality questionnaires to the users, making the process very costly and slow, and hindering research on large populations. In this paper, we demonstrate that it is possible to predict which images are most associated with each personality trait of the OCEAN personality model, without requiring ground-truth personality labels. Namely, we present a weakly supervised framework which shows that the personality scores obtained using specific images textually associated with particular personality traits are highly correlated with scores obtained using standard text-based personality questionnaires. We trained an OCEAN trait model based on Convolutional Neural Networks (CNNs), learned from 120K pictures posted with specific textual hashtags, to infer whether the personality scores from the images uploaded by users are consistent with those scores obtained from text. In order to validate our claims, we performed a personality test on a heterogeneous group of 280 human subjects, showing that our model successfully predicts which kind of image will match a person with a given level of a trait. Looking at the results, we obtained evidence that personality is not only correlated with text, but with image content too. Interestingly, different visual patterns emerged from those images most liked by persons with a particular personality trait: for instance, pictures most associated with high conscientiousness usually contained healthy food, while low conscientiousness pictures contained injuries, guns, and alcohol. These findings could pave the way to complement text-based personality questionnaires with image-based questions. Full article
(This article belongs to the Special Issue Sentiment Analysis for Social Media Ⅱ)
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Open AccessArticle
Combining Post Sentiments and User Participation for Extracting Public Stances from Twitter
Appl. Sci. 2020, 10(22), 8035; https://doi.org/10.3390/app10228035 - 12 Nov 2020
Abstract
With the wide popularity of social media, it’s becoming more convenient for people to express their opinions online. To better understand what the public think about a topic, sentiment classification techniques have been widely used to estimate the overall orientation of opinions in [...] Read more.
With the wide popularity of social media, it’s becoming more convenient for people to express their opinions online. To better understand what the public think about a topic, sentiment classification techniques have been widely used to estimate the overall orientation of opinions in post contents. However, users might have various degrees of influence depending on their participation in discussions on different topics. In this paper, we address the issues of combining sentiment classification and link analysis techniques for extracting stances of the public from social media. Since social media posts are usually very short, word embedding models are first used to learn different word usages in various contexts. Then, deep learning methods such as Long Short-Term Memory (LSTM) are used to learn the long-distance context dependency among words for better estimation of sentiments. Third, we consider the major user participation in popular social media by adjusting the users weights to reflect their relative influence in user-post interaction graphs. Finally, we combine post sentiments and user influences into a total opinion score for extracting public stances. In the experiments, we evaluated the performance of our proposed approach for tweets about the 2016 U.S. Presidential Election. The best performance of sentiment classification can be observed with an F-measure of 72.97% for LSTM classifiers. This shows the effectiveness of deep learning methods in learning word usage in social media contexts. The experimental results on stance extraction showed the best performance of 0.68% Mean Absolute Error (MAE) in aggregating public stances on election candidates. This shows the potential of combining tweet sentiments and user participation structures for extracting the aggregate stances of the public on popular topics. Further investigation is needed to verify the performance in different social media sources. Full article
(This article belongs to the Special Issue Sentiment Analysis for Social Media Ⅱ)
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Open AccessFeature PaperArticle
Time of Your Hate: The Challenge of Time in Hate Speech Detection on Social Media
Appl. Sci. 2020, 10(12), 4180; https://doi.org/10.3390/app10124180 - 18 Jun 2020
Cited by 3
Abstract
The availability of large annotated corpora from social media and the development of powerful classification approaches have contributed in an unprecedented way to tackle the challenge of monitoring users’ opinions and sentiments in online social platforms across time. Such linguistic data are strongly [...] Read more.
The availability of large annotated corpora from social media and the development of powerful classification approaches have contributed in an unprecedented way to tackle the challenge of monitoring users’ opinions and sentiments in online social platforms across time. Such linguistic data are strongly affected by events and topic discourse, and this aspect is crucial when detecting phenomena such as hate speech, especially from a diachronic perspective. We address this challenge by focusing on a real case study: the “Contro l’odio” platform for monitoring hate speech against immigrants in the Italian Twittersphere. We explored the temporal robustness of a BERT model for Italian (AlBERTo), the current benchmark on non-diachronic detection settings. We tested different training strategies to evaluate how the classification performance is affected by adding more data temporally distant from the test set and hence potentially different in terms of topic and language use. Our analysis points out the limits that a supervised classification model encounters on data that are heavily influenced by events. Our results show how AlBERTo is highly sensitive to the temporal distance of the fine-tuning set. However, with an adequate time window, the performance increases, while requiring less annotated data than a traditional classifier. Full article
(This article belongs to the Special Issue Sentiment Analysis for Social Media Ⅱ)
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Open AccessArticle
Using Keystroke Dynamics in a Multi-Agent System for User Guiding in Online Social Networks
Appl. Sci. 2020, 10(11), 3754; https://doi.org/10.3390/app10113754 - 28 May 2020
Cited by 2
Abstract
Nowadays there is a strong integration of online social platforms and applications with our daily life. Such interactions can make risks arise and compromise the information we share, thereby leading to privacy issues. In this work, a proposal that makes use of a [...] Read more.
Nowadays there is a strong integration of online social platforms and applications with our daily life. Such interactions can make risks arise and compromise the information we share, thereby leading to privacy issues. In this work, a proposal that makes use of a software agent that performs sentiment analysis and another performing stress analysis on keystroke dynamics data has been designed and implemented. The proposal consists of a set of new agents that have been integrated into a multi-agent system (MAS) for guiding users interacting in online social environments, which has agents for sentiment and stress analysis on text. We propose a combined analysis using the different agents. The MAS analyzes the states of the users when they are interacting, and warns them if the messages they write are deemed negative. In this way, we aim to prevent potential negative outcomes on social network sites (SNSs). We performed experiments in the laboratory with our private SNS Pesedia over a period of one month, so we gathered data about text messages and keystroke dynamics data, and used the datasets to train the artificial neural networks (ANNs) of the agents. A set of experiments was performed for discovering which analysis is able to detect a state of the user that propagates more in the SNS, so it may be more informative for the MAS. Our study will help develop future intelligent systems that utilize user data in online social environments for guiding or helping them in their social experience. Full article
(This article belongs to the Special Issue Sentiment Analysis for Social Media Ⅱ)
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Open AccessArticle
CRANK: A Hybrid Model for User and Content Sentiment Classification Using Social Context and Community Detection
Appl. Sci. 2020, 10(5), 1662; https://doi.org/10.3390/app10051662 - 01 Mar 2020
Abstract
Recent works have shown that sentiment analysis on social media can be improved by fusing text with social context information. Social context is information such as relationships between users and interactions of users with content. Although existing works have already exploited the networked [...] Read more.
Recent works have shown that sentiment analysis on social media can be improved by fusing text with social context information. Social context is information such as relationships between users and interactions of users with content. Although existing works have already exploited the networked structure of social context by using graphical models or techniques such as label propagation, more advanced techniques from social network analysis remain unexplored. Our hypothesis is that these techniques can help reveal underlying features that could help with the analysis. In this work, we present a sentiment classification model (CRANK) that leverages community partitions to improve both user and content classification. We evaluated this model on existing datasets and compared it to other approaches. Full article
(This article belongs to the Special Issue Sentiment Analysis for Social Media Ⅱ)
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