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Information
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Sentiment Analysis and Affective Computing
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Sentiment Analysis and Affective Computing

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Processes".

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 32654

Special Issue Editors

Dr. Salud María Jiménez-Zafra

E-Mail Website
Guest Editor
SINAI Research Group, Computer Science Department, CEATIC, Universidad de Jaén, 23071 Jaén, Spain
Interests: natural language processing; negation detection and treatment; semantics; text mining
Special Issues, Collections and Topics in MDPI journals
Dr. Miguel Ángel García Cumbreras

E-Mail Website
Guest Editor
SINAI Research Group, Computer Science Department, CEATIC, Universidad de Jaén, 23071 Jaén, Spain
Interests: sentiment analysis; affective computing; fake news; question answering; machine learning; natural language processing

Special Issue Information

Dear Colleagues,

The publication of contents, personal experiences and opinions about anything in social media (forums, blogs, social networks, etc.) has aroused great interest in Sentiment Analysis and Affective Computing, since they provide substantial benefits for different sectors. These disciplines are concerned with the study of the opinions, emotional states and human-behaviour expressed in texts.  The automatic detection of this information is of great value for improving business strategies according to the opinions and emotions of customers, detecting signs of depression, identifying cases of cyber-bullying, hate speech or toxic comments, or even for the development of efficient e-learning systems based on student’s emotions.

Although deep learning models are providing breakthrough results in conjunction with large datasets, Sentiment Analysis and Affective Computing are still challenging areas of Natural Language Processing, because these large datasets are not always available.

This special issue is aimed at theoretical or experimental works on Sentiment Analysis or Affective Computing in social media, for languages with limited resources, methods to analyze social behaviors, processing linguistic phenomena (negation, irony, sarcasm, etc.), hate-speech detection, fine-grained sentiment analysis, identification of psychological states such as depression, domain-dependent information, transfer learning issues, multilingual aspects, personalized sentiment analysis,  etc.

Topics of interest include but are not limited to:

  • Aspect-based sentiment analysis
  • Multilingual sentiment analysis
  • Personalized sentiment analysis
  • Sentiment analysis or affective computing in languages with few resources
  • Resources for sentiment analysis or affective computing
  • Negation processing for improving sentiment analysis or affective computing
  • Irony or sarcasm detection for improving sentiment analysis or affective computing
  • Transfer learning for sentiment analysis or affective computing
  • Emotion mining in social media
  • Hate-speech detection
  • Identification of psychological states such as depression
  • Cyber-bullying detection
  • Offensive language identification

Dr. Salud María Jiménez-Zafra
Dr. Miguel Ángel García Cumbreras
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. Information is an international peer-reviewed open access monthly 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 1600 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
  • Affective Computing
  • Social media
  • Opinion Mining
  • Emotion Mining
  • Natural Language Processing
  • Machine Learning
  • Deep Learning

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Published Papers (7 papers)

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Research

16 pages, 473 KiB  
Article
Multimodal Fake News Detection
by Isabel Segura-Bedmar and Santiago Alonso-Bartolome
Information 2022, 13(6), 284; https://doi.org/10.3390/info13060284 - 2 Jun 2022
Cited by 45 | Viewed by 8766
Abstract
Over the last few years, there has been an unprecedented proliferation of fake news. As a consequence, we are more susceptible to the pernicious impact that misinformation and disinformation spreading can have on different segments of our society. Thus, the development of tools [...] Read more.
Over the last few years, there has been an unprecedented proliferation of fake news. As a consequence, we are more susceptible to the pernicious impact that misinformation and disinformation spreading can have on different segments of our society. Thus, the development of tools for the automatic detection of fake news plays an important role in the prevention of its negative effects. Most attempts to detect and classify false content focus only on using textual information. Multimodal approaches are less frequent and they typically classify news either as true or fake. In this work, we perform a fine-grained classification of fake news on the Fakeddit dataset, using both unimodal and multimodal approaches. Our experiments show that the multimodal approach based on a Convolutional Neural Network (CNN) architecture combining text and image data achieves the best results, with an accuracy of 87%. Some fake news categories, such as Manipulated content, Satire, or False connection, strongly benefit from the use of images. Using images also improves the results of the other categories but with less impact. Regarding the unimodal approaches using only text, Bidirectional Encoder Representations from Transformers (BERT) is the best model, with an accuracy of 78%. Exploiting both text and image data significantly improves the performance of fake news detection. Full article
(This article belongs to the Special Issue Sentiment Analysis and Affective Computing)
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26 pages, 6382 KiB  
Article
Towards Automated Semantic Explainability of Multimedia Feature Graphs
by Stefan Wagenpfeil, Paul Mc Kevitt and Matthias Hemmje
Information 2021, 12(12), 502; https://doi.org/10.3390/info12120502 - 2 Dec 2021
Cited by 4 | Viewed by 2967
Abstract
Multimedia feature graphs are employed to represent features of images, video, audio, or text. Various techniques exist to extract such features from multimedia objects. In this paper, we describe the extension of such a feature graph to represent the meaning of such multimedia [...] Read more.
Multimedia feature graphs are employed to represent features of images, video, audio, or text. Various techniques exist to extract such features from multimedia objects. In this paper, we describe the extension of such a feature graph to represent the meaning of such multimedia features and introduce a formal context-free PS-grammar (Phrase Structure grammar) to automatically generate human-understandable natural language expressions based on such features. To achieve this, we define a semantic extension to syntactic multimedia feature graphs and introduce a set of production rules for phrases of natural language English expressions. This explainability, which is founded on a semantic model provides the opportunity to represent any multimedia feature in a human-readable and human-understandable form, which largely closes the gap between the technical representation of such features and their semantics. We show how this explainability can be formally defined and demonstrate the corresponding implementation based on our generic multimedia analysis framework. Furthermore, we show how this semantic extension can be employed to increase the effectiveness in precision and recall experiments. Full article
(This article belongs to the Special Issue Sentiment Analysis and Affective Computing)
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16 pages, 1327 KiB  
Article
Exploring the Impact of COVID-19 on Social Life by Deep Learning
by Jose Antonio Jijon-Vorbeck and Isabel Segura-Bedmar
Information 2021, 12(11), 459; https://doi.org/10.3390/info12110459 - 5 Nov 2021
Cited by 3 | Viewed by 2933
Abstract
Due to the globalisation of the COVID-19 pandemic, and the expansion of social media as the main source of information for many people, there have been a great variety of different reactions surrounding the topic. The World Health Organization (WHO) announced in December [...] Read more.
Due to the globalisation of the COVID-19 pandemic, and the expansion of social media as the main source of information for many people, there have been a great variety of different reactions surrounding the topic. The World Health Organization (WHO) announced in December 2020 that they were currently fighting an “infodemic” in the same way as they were fighting the pandemic. An “infodemic” relates to the spread of information that is not controlled or filtered, and can have a negative impact on society. If not managed properly, an aggressive or negative tweet can be very harmful and misleading among its recipients. Therefore, authorities at WHO have called for action and asked the academic and scientific community to develop tools for managing the infodemic by the use of digital technologies and data science. The goal of this study is to develop and apply natural language processing models using deep learning to classify a collection of tweets that refer to the COVID-19 pandemic. Several simpler and widely used models are applied first and serve as a benchmark for deep learning methods, such as Long Short-Term Memory (LSTM) and Bidirectional Encoder Representations from Transformers (BERT). The results of the experiments show that the deep learning models outperform the traditional machine learning algorithms. The best approach is the BERT-based model. Full article
(This article belongs to the Special Issue Sentiment Analysis and Affective Computing)
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13 pages, 347 KiB  
Article
Emotion Classification in Spanish: Exploring the Hard Classes
by Aiala Rosá and Luis Chiruzzo
Information 2021, 12(11), 438; https://doi.org/10.3390/info12110438 - 21 Oct 2021
Cited by 5 | Viewed by 2984
Abstract
The study of affective language has had numerous developments in the Natural Language Processing area in recent years, but the focus has been predominantly on Sentiment Analysis, an expression usually used to refer to the classification of texts according to their polarity or [...] Read more.
The study of affective language has had numerous developments in the Natural Language Processing area in recent years, but the focus has been predominantly on Sentiment Analysis, an expression usually used to refer to the classification of texts according to their polarity or valence (positive vs. negative). The study of emotions, such as joy, sadness, anger, surprise, among others, has been much less developed and has fewer resources, both for English and for other languages, such as Spanish. In this paper, we present the most relevant existing resources for the study of emotions, mainly for Spanish; we describe some heuristics for the union of two existing corpora of Spanish tweets; and based on some experiments for classification of tweets according to seven categories (anger, disgust, fear, joy, sadness, surprise, and others) we analyze the most problematic classes. Full article
(This article belongs to the Special Issue Sentiment Analysis and Affective Computing)
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14 pages, 739 KiB  
Article
Feature Extraction Network with Attention Mechanism for Data Enhancement and Recombination Fusion for Multimodal Sentiment Analysis
by Qingfu Qi, Liyuan Lin and Rui Zhang
Information 2021, 12(9), 342; https://doi.org/10.3390/info12090342 - 24 Aug 2021
Cited by 8 | Viewed by 3277
Abstract
Multimodal sentiment analysis and emotion recognition represent a major research direction in natural language processing (NLP). With the rapid development of online media, people often express their emotions on a topic in the form of video, and the signals it transmits are multimodal, [...] Read more.
Multimodal sentiment analysis and emotion recognition represent a major research direction in natural language processing (NLP). With the rapid development of online media, people often express their emotions on a topic in the form of video, and the signals it transmits are multimodal, including language, visual, and audio. Therefore, the traditional unimodal sentiment analysis method is no longer applicable, which requires the establishment of a fusion model of multimodal information to obtain sentiment understanding. In previous studies, scholars used the feature vector cascade method when fusing multimodal data at each time step in the middle layer. This method puts each modal information in the same position and does not distinguish between strong modal information and weak modal information among multiple modalities. At the same time, this method does not pay attention to the embedding characteristics of multimodal signals across the time dimension. In response to the above problems, this paper proposes a new method and model for processing multimodal signals, which takes into account the delay and hysteresis characteristics of multimodal signals across the time dimension. The purpose is to obtain a multimodal fusion feature emotion analysis representation. We evaluate our method on the multimodal sentiment analysis benchmark dataset CMU Multimodal Opinion Sentiment and Emotion Intensity Corpus (CMU-MOSEI). We compare our proposed method with the state-of-the-art model and show excellent results. Full article
(This article belongs to the Special Issue Sentiment Analysis and Affective Computing)
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17 pages, 572 KiB  
Article
A Survey on Sentiment Analysis and Opinion Mining in Greek Social Media
by Georgios Alexandridis, Iraklis Varlamis, Konstantinos Korovesis, George Caridakis and Panagiotis Tsantilas
Information 2021, 12(8), 331; https://doi.org/10.3390/info12080331 - 18 Aug 2021
Cited by 29 | Viewed by 7010
Abstract
As the amount of content that is created on social media is constantly increasing, more and more opinions and sentiments are expressed by people in various subjects. In this respect, sentiment analysis and opinion mining techniques can be valuable for the automatic analysis [...] Read more.
As the amount of content that is created on social media is constantly increasing, more and more opinions and sentiments are expressed by people in various subjects. In this respect, sentiment analysis and opinion mining techniques can be valuable for the automatic analysis of huge textual corpora (comments, reviews, tweets etc.). Despite the advances in text mining algorithms, deep learning techniques, and text representation models, the results in such tasks are very good for only a few high-density languages (e.g., English) that possess large training corpora and rich linguistic resources; nevertheless, there is still room for improvement for the other lower-density languages as well. In this direction, the current work employs various language models for representing social media texts and text classifiers in the Greek language, for detecting the polarity of opinions expressed on social media. The experimental results on a related dataset collected by the authors of the current work are promising, since various classifiers based on the language models (naive bayesian, random forests, support vector machines, logistic regression, deep feed-forward neural networks) outperform those of word or sentence-based embeddings (word2vec, GloVe), achieving a classification accuracy of more than 80%. Additionally, a new language model for Greek social media has also been trained on the aforementioned dataset, proving that language models based on domain specific corpora can improve the performance of generic language models by a margin of 2%. Finally, the resulting models are made freely available to the research community. Full article
(This article belongs to the Special Issue Sentiment Analysis and Affective Computing)
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11 pages, 214 KiB  
Article
Finding Evidence of Fraudster Companies in the CEO’s Letter to Shareholders with Sentiment Analysis
by Núria Bel, Gabriel Bracons and Sophia Anderberg
Information 2021, 12(8), 307; https://doi.org/10.3390/info12080307 - 30 Jul 2021
Cited by 5 | Viewed by 2810
Abstract
The goal of our research was to assess whether the observation about deceptive texts having a lower positive tone than truthful ones in terms of sentiment could become operative and be used for building a classifier in the particular case of fraudster’s letters [...] Read more.
The goal of our research was to assess whether the observation about deceptive texts having a lower positive tone than truthful ones in terms of sentiment could become operative and be used for building a classifier in the particular case of fraudster’s letters written in Spanish. The data were the letters that CEOs address to company shareholders in their annual financial reports, and the task was to identify the letters of companies that committed financial misconduct or fraud. This case was challenging for two reasons: first, most of the research worked with spontaneous written or spoken texts, while these letters did not; second, most of the research in this area worked on English texts, while we validated the linguistic cues found as evidence of deception for Spanish texts. The results of our research confirm that an SVM trained with a bag-of-words model of frequent adjectives can achieve 81% accuracy because these adjectives bring the information about which positive or negative tone and which word combinations in a text turn out to be a characteristic of fraudster’s texts. Full article
(This article belongs to the Special Issue Sentiment Analysis and Affective Computing)
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