Automatic detection of satire in Twitter: A psycholinguistic-based approach

Salas-Zrate, Mara del Pilar; Paredes-Valverde, Mario Andrs; Rodriguez-Garca, Miguel ngel; Valencia-Garca, Rafael; Alor-Hernndez, Giner

doi:10.1016/j.knosys.2017.04.009

Cited by 59 publications

(51 citation statements)

References 36 publications

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“…For example, Linguistic Inquiry and Word Count (LIWC) [38] is a text analysis programme that counts words within pre-established psychological categories that capture content words which convey what people are saying (nouns, verbs or adjectives), and style words that convey how people are expressing something (prepositions, articles, conjunctions or auxiliary verbs). LIWC has been applied in several areas, such as suicide [39], cyber-bulling [40], and satire detection [41]. LIWC is a language-dependent tool that has been translated into several languages, including Spanish [42].…”

Section: Feature Engineeringmentioning

confidence: 99%

“…We distinguish between (1) sentence dividers, such as spaces, colons, and semicolons, in order to capture the rhythm of the text, and (2) general-purpose symbols, such as those used to express measures as units. We additionally captured some of the terms specifically employed in Twitter, such as hashtags, mentions or hyperlinks, because they have been proven to be discriminating features for Sentiment Analysis [41].…”

Section: • Figurative Language (Fig)mentioning

confidence: 99%

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Ontology-driven aspect-based sentiment analysis classification: An infodemiological case study regarding infectious diseases in Latin America

García-Díaz

Cánovas-García

Valencia‐García

2020

Future Generation Computer Systems

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Infodemiology is the process of mining unstructured and textual data so as to provide public health officials and policymakers with valuable information regarding public health. The appearance of this new data source, which was previously unimaginable, has opened up a new way in which to improve public health systems, resulting in better communication policies and better detection systems. However, the unstructured nature of the Internet, along with the complexity of the infectious disease domain, prevents the information extracted from being easily understood. Moreover, when dealing with languages other than English, for which some of the most common Natural Language Processing resources are not available, the correct exploitation of this data becomes even more difficult. We intend to fill these gaps proposing an ontology-driven aspect-based sentiment analysis with which to measure the general public's opinions as regards infectious diseases when expressed in Spanish by employing a case study of tweets concerning the Zika, Dengue and Chikungunya viruses in Latin America. Our proposal is based on two technologies. We first use ontologies in order to model the infectious disease domain with concepts such as risks, symptoms, transmission methods or drugs, among other concepts. We then measure the relationship between these concepts in order to determine the degree to which one concept influences other concepts. This new information is subsequently applied in order to build an aspect-based sentiment analysis model based on statistical and linguistic features. This is done by applying deep-learning models. Our proposal is available on a web platform, where users can see the sentiment for each concept at a glance and analyse how each concept influences the sentiment of the others.

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Section: Feature Engineeringmentioning

confidence: 99%

Section: • Figurative Language (Fig)mentioning

confidence: 99%

Ontology-driven aspect-based sentiment analysis classification: An infodemiological case study regarding infectious diseases in Latin America

García-Díaz

Cánovas-García

Valencia‐García

2020

Future Generation Computer Systems

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“…People tend to accept only what they want to believe, and if they repeatedly exposed to the wrong information, they are very likely to accept it [21]. Generally, materials related to fake news spreading in social media have the following commonalities: satire, parody, misinterpretation, foment, and heavily biased contents [35][36][37][38].…”

Section: Fake Newsmentioning

confidence: 99%

Fake News Analysis Modeling Using Quote Retweet

2019

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Fake news can confuse many people in the area of politics, culture, healthcare, etc. Fake news refers to news containing misleading or fabricated contents that are actually groundless; they are intentionally exaggerated or provide false information. As such, fake news can distort reality and cause social problems, such as self-misdiagnosis of medical issues. Many academic researchers have been collecting data from social and medical media, which are sources of various information flows, and conducting studies to analyse and detect fake news. However, in the case of conventional studies, the features used for analysis are limited, and the consideration for newly added features of social media is lacking. Therefore, this study proposes a fake news analysis modelling method by identifying a variety of features and collecting various data from Twitter, a social media outlet with a good deal of power in terms of spreading information. The method proposed in this study can increase the accuracy of fake news analysis by acquiring more potential information from the Quote Retweet feature added to Twitter in 2015, compared to the more conventional and common Retweet only. Furthermore, fake news was analysed through neural network-based classification modelling by using the preprocessed data and the identified best features in the learning data. In the performance results, using the neural network-based classifier, the classification model that also used Quote Retweet, showed an improvement in performance over the conventional methods, and it was confirmed that the identified best features had a significant impact on increasing the classification accuracy of fake news.

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“…Figure 2 presents a tweet with some of these issues. In order to deal with this problem, we adopted the approach presented in [25] for the tweets processing. The first phase of the preprocessing module consists in the tokenization process.…”

Section: Preprocessingmentioning

confidence: 99%

Sentiment Analysis in Spanish for Improvement of Products and Services: A Deep Learning Approach

Paredes-Valverde

Colomo‐Palacios

Salas-Zárate

et al. 2017

Scientific Programming

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Sentiment analysis is an important area that allows knowing public opinion of the users about several aspects. This information helps organizations to know customer satisfaction. Social networks such as Twitter are important information channels because information in real time can be obtained and processed from them. In this sense, we propose a deep-learning-based approach that allows companies and organizations to detect opportunities for improving the quality of their products or services through sentiment analysis. This approach is based on convolutional neural network (CNN) and word2vec. To determine the effectiveness of this approach for classifying tweets, we conducted experiments with different sizes of a Twitter corpus composed of 100000 tweets. We obtained encouraging results with a precision of 88.7%, a recall of 88.7%, and an -measure of 88.7% considering the complete dataset.

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Automatic detection of satire in Twitter: A psycholinguistic-based approach

Cited by 59 publications

References 36 publications

Ontology-driven aspect-based sentiment analysis classification: An infodemiological case study regarding infectious diseases in Latin America

Ontology-driven aspect-based sentiment analysis classification: An infodemiological case study regarding infectious diseases in Latin America

Fake News Analysis Modeling Using Quote Retweet

Sentiment Analysis in Spanish for Improvement of Products and Services: A Deep Learning Approach

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