A Method Proposed for Estimating Depressed Feeling Tendencies of Social Media Users Utilizing Their Data

Birjali, Marouane; Beni‐Hssane, Abderrahim; Erritali, Mohammed

doi:10.1007/978-3-319-52941-7_41

Cited by 15 publications

(7 citation statements)

References 4 publications

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“…Os usuários foram extraídos do conjunto de 180 usuários (90 depressivos e 90 não depressivos) que foram detectados por um grupo de psicólogos em [30]. [24] IBK, J48, SMO; PART Twitter Automática - [25] PLN; SVM; Random Forest Reddit Manual - [26] PLN juntamente com Lexicos Fórum de discussão PTT Manual - [27] CNN; NB; KNN Notas escritas em Árabe Manual - [28] PLN juntamente com LDA Twitter e blogs de depressão Automática - [29] SVM; LIWC Twitter Manual -…”

Section: Resultados E Discussõesunclassified

“…Os autores Birjali, Beni-Hssane e Erritali [24] optaram por buscar postagens do Twitter e as rotular de forma automática utilizando para isso o método de similaridade semântica WordNet tendo como base um "vocabulário da depressão" definido previamente.…”

Section: • Rotulação Automáticaunclassified

“…Diferentes estudos [7], [8], [12], [15], [17], [18], [21], [22], [24] e [28] utilizaram o Twitter como fonte de dados para criação das bases de dados. Dentre tais estudos, é importante destacar o estudo de Nikhita e Srinivasan [18], o qual teve como objetivo identificar se existe uma diferenciação no comportamento online entre usuários potencialmente deprimidos, com relação a usuários normais.…”

Section: Análise E Discussãounclassified

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Identificação de Perfis Depressivos em Redes Sociais Utilizando Aprendizado de Máquina: um Mapeamento Sistemático

Casani¹,

Mantovani²,

Souza³

et al. 2020

Anais Do XI Computer on the Beach - COTB '20

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Depression is a psychological disorder that affects millions of peoplein theworld, regardless of their age, social class or nationality. In theliterature, different techniques have been studying to analyze andrecognize this disease such as Natural Language Processing, SentimentAnalysis, and Machine Learning. In this paper, we describe asystematic mapping to identify evidence regarding techniques thatare often used to identify depressive profiles.We analyzed 472 studiesand we selected 25 primary studies. These studies indicate thatthe SVM and NB techniques have been most used to detect possibledepressive profiles in social networks. Furthermore, Twitter andFacebook with 35,5% and 22,6%, respectively were the social mediamost have been used by users’ express their feelings regarding themost varied subjects.

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Section: • Rotulação Automáticaunclassified

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Identificação de Perfis Depressivos em Redes Sociais Utilizando Aprendizado de Máquina: um Mapeamento Sistemático

Casani¹,

Mantovani²,

Souza³

et al. 2020

Anais Do XI Computer on the Beach - COTB '20

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“…Popular sentiment analysis methods are based on machine learning algorithms that use several feature extraction models 8‐10 . The Bag of Word (BOW) model is used in most of them, which consists of mapping each sentence to a features vector in order to be exploited by machine learning algorithms 11‐13 . One of the main advantages of this model is simplicity.…”

Section: Introductionmentioning

confidence: 99%

Word2Sent: A new learning sentiment‐embedding model with low dimension for sentence level sentiment classification

Kasri

Birjali

Beni‐Hssane

2020

Concurrency and Computation

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Word embedding models become an increasingly important method that embeds words into a high dimensional space. These models have been widely utilized to extract semantic and syntactic features for sentiment analysis. However, using word embedding models cannot be sufficient for sentiment analysis tasks because they do not contain sentiment features. Therefore, word embedding models do not adequately meet the comprehensive needs of sentiment analysis applications that rely on recognizing the polarity of a sentence. In this paper, we propose a sentiment embedding model (Word2Sent model) to tackle the weaknesses of the existing word embedding models for sentiment analysis applications. We developed this model based on the Continuous Bag‐of‐Words model and SentiWordNet lexicon to learn sentiment embedding for each word from its surrounding context words. It preserves semantic and syntactic features and captures implicitly sentiment ones. Besides, it can predict sentiment features in a very low sentiment embeddings dimension than traditional ones. The proposed method provides an improved sentiment classification performance and lowers the computational complexity. Both the accuracy performance and processing time results obtained indicate that the proposed model is particularly promising.

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“…Twitter is the most explored social network. Studies that rely only on Twitter as a data source are WANG et al, 2013;CHOUDHURY et al, 2013a;BIRJALI;BENI-HSSANE;ERRITALI, 2017;SHEN et al, 2017;VEDULA;PARTHASARATHY, 2017a;LACHMAR et al, 2017). In their work, (HASSAN et al, 2017) combined Twitter with another local social network, and (JUNG; PARK; SONG, 2017) built their dataset combining data from Twitter, Blogs and Forum, and another local social network.…”

Section: Social Network Used In the Studiesmentioning

confidence: 99%

An approach to the sequential evaluation of emotional behaviors of depressive users on social networks in groups and individually

Giuntini¹

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Carlos, for the necessary support, discussion, and validation of the results with the view of psychology and behavior analysis.• My friends that I met in the city of São Carlos-SP and were essential for me to get here.• My colleagues, friends, and professors at Laboratório Intermídia for the reception, discussion, and friendship.• The Institute of Mathematical and Computer Sciences (ICMC) of the University of São Paulo (USP) and the professors and employees involved.

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A Method Proposed for Estimating Depressed Feeling Tendencies of Social Media Users Utilizing Their Data

Cited by 15 publications

References 4 publications

Identificação de Perfis Depressivos em Redes Sociais Utilizando Aprendizado de Máquina: um Mapeamento Sistemático

Identificação de Perfis Depressivos em Redes Sociais Utilizando Aprendizado de Máquina: um Mapeamento Sistemático

Word2Sent: A new learning sentiment‐embedding model with low dimension for sentence level sentiment classification

An approach to the sequential evaluation of emotional behaviors of depressive users on social networks in groups and individually

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