A Knowledge-Based Recommendation System That Includes Sentiment Analysis and Deep Learning

Rosa, Renata Lopes; Schwartz, Gisele Maria; Ruggiero, Wilson Vicente; Rodríguez, Demóstenes Zegarra

doi:10.1109/tii.2018.2867174

Cited by 157 publications

(86 citation statements)

References 45 publications

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“…A análise referente as bases de dados citadas pelos estudos possibilitou identificar que apenas o estudo [13] teve sua base criada em português. Isto indica o deficit de estudos focados em analisar sentimentos no contexto de doenças mentais em português.…”

Section: Resultados E Discussõesunclassified

“…O estudo de Rosa et. al [13] filtrou sentenças providas de postagens do Facebook que continham expressões como "odeio minha vida", "me sentindo triste", "estou estressado", entre outras. Também utilizou um conjunto de sentenças positivas para classificar como "não depressão".…”

Section: • Rotulação Automáticaunclassified

“…Os estudos [9], [12], [13], [14], [17], [17] e [20] utilizaram postagens do Facebook para compor suas respectivas base de dados de treinamento e testes. Dentre eles destaca-se o estudo de Rosa et.…”

Section: Análise E Discussãounclassified

“…Dentre eles destaca-se o estudo de Rosa et. al [13] por ser o único estudo dentre todos os 25 que utilizou uma fonte de dados em português. Os autores realizaram o desenvolvimento de uma aplicação web que tem como objetivo servir como um sistema de recomendação para aliviar sintomas da depressão, para isso foi criado uma base de textos a partir de ontologias.…”

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: Resultados E Discussõesunclassified

Section: • Rotulação Automáticaunclassified

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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“…Besides these, Araque et al attempted to improve the performance of sentiment analysis by integrating deep learning techniques with traditional surface approaches [14]. A knowledge-based recommendation system (KBRS) was presented to detect users with potential psychological disturbances based on sentiment analysis [15]. Irsoy and Cardie implemented a deep recurrent neural network for opinion expression extraction [16].…”

Section: Sentiment Analysis With Deep Learningmentioning

confidence: 99%

Sentiment Analysis Based Requirement Evolution Prediction

Zhao

2019

Future Internet

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To facilitate product developers capturing the varying requirements from users to support their feature evolution process, requirements evolution prediction from massive review texts is in fact of great importance. The proposed framework combines a supervised deep learning neural network with an unsupervised hierarchical topic model to analyze user reviews automatically for product feature requirements evolution prediction. The approach is to discover hierarchical product feature requirements from the hierarchical topic model and to identify their sentiment by the Long Short-term Memory (LSTM) with word embedding, which not only models hierarchical product requirement features from general to specific, but also identifies sentiment orientation to better correspond to the different hierarchies of product features. The evaluation and experimental results show that the proposed approach is effective and feasible.

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Nonlinear non‐Gaussian and multimode probabilistic weighted copula regression model based deep neural network

Chang

2021

Can J Chem Eng

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A probabilistic weighted copula (WCR) approach based on a sparse robust auto-encoder (SRAE) is proposed. For the industrial processes, which have multi-operation conditions, SRAE model with the five-layer stacked autoencoder is introduced to do feature learning. Then, the extracted feature can be used to classify the multi-operation conditions. Besides, the advanced highlevel features extracted by the SRAE model can deal with the nonlinear and non-Gaussian process well. The WCR model applies the vine copula function and Monte Carlo estimation to quickly depict the distribution of the process.

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A Knowledge-Based Recommendation System That Includes Sentiment Analysis and Deep Learning

Cited by 157 publications

References 45 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

Sentiment Analysis Based Requirement Evolution Prediction

Nonlinear non‐Gaussian and multimode probabilistic weighted copula regression model based deep neural network

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