scholarly journals ANÁLISE DE SENTIMENTO PARA REVIEWS APRESENTADOS EM VÍDEOS: MODELO DE REDES NEURAIS TREINADO EM BASE DE REVIEWS ESCRITOS/ SENTIMENT ANALYSIS FOR REVIEWS FEATURED IN VIDEOS: NEURAL NETWORK MODEL TRAINED ON WRITTEN REVIEWS DATABASE

2020 ◽  
Vol 4 (1) ◽  
pp. 2-19
Author(s):  
João Paulo Vieira Costa ◽  
Romulo Baldez de Barros ◽  
Caio César Silva Dantas ◽  
Raquel Cristina de Sousa ◽  
Cristiano Gonçalves Nascimento Gouveia ◽  
...  
Keyword(s):  
Neural Network ◽  
Sentiment Analysis ◽  
Network Model ◽  
Neural Network Model

Implicit sentiment analysis based on multi-feature neural network model

2021 ◽  
Author(s):  
Yin Zhuang ◽  
Zhen Liu ◽  
Ting-Ting Liu ◽  
Chih-Chieh Hung ◽  
Yan-Jie Chai
Keyword(s):  
Neural Network ◽  
Sentiment Analysis ◽  
Network Model ◽  
Neural Network Model

Sarcasm Detection Using RNN with Relation Vector

2019 ◽  
Vol 15 (4) ◽  
pp. 66-78 ◽  
Author(s):  
Satoshi Hiai ◽  
Kazutaka Shimada
Keyword(s):  
Neural Network ◽  
Sentiment Analysis ◽  
Network Model ◽  
Recurrent Neural Network ◽  
Neural Network Model ◽  
Detection Method ◽  
Surface Information ◽  
Significant Challenge ◽  
Classification Feature ◽  
Positive Expression

Sarcasm detection has been treated as a task that classifies text as sarcastic or non-sarcastic. Sarcasm detection is a significant challenge for sentiment analysis because sarcasm involves a positive expression with a negative meaning. Surface information in text is commonly used as a classification feature. However, the authors must consider both surface and non-surface features. In this article, the authors focus on relation information between pairs of role expressions, such as “boss and staff,” and propose a sarcasm detection method based on surface and relation information. First, the authors extract role pairs from a corpus. Then, the authors construct a relation vector generated from these role pairs and incorporate the relation vector into a recurrent neural network model. The authors evaluated the proposed method by comparing it to previously proposed methods. The results demonstrate the effectiveness of introducing the relation vector to sarcasm detection.

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