A Sentiment Analysis Method for Facial Expression Generation in Human-Robot Interactive Communication

2014 ◽  
Author(s):  
Yu Bai ◽  
Xiaoli Tang ◽  
Yaohui Zhu ◽  
Dongfeng Cai
Keyword(s):  
Facial Expression ◽  
Sentiment Analysis ◽  
Analysis Method ◽  
Interactive Communication

scholarly journals Sentiment Analysis Method of Network Text Based on Improved AT-BiGRU Model

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xinxin Lu ◽  
Hong Zhang
Keyword(s):  
Sentiment Analysis ◽  
Current Method ◽  
Recurrent Network ◽  
Analysis Method ◽  
Large Space ◽  
Spelling Errors ◽  
Training Time ◽  
Proposed Model ◽  
Correct Spelling ◽  
Text Sentiment Analysis

In order to solve the problems existing in the current method of emotional analysis of network text, such as long training time, complex calculation, and large space cost, this paper proposes an Internet text sentiment analysis method based on the improved AT-BiGRU model. Firstly, the textblob package is imported to correct spelling errors before text preprocessing. Secondly, pad_sequences are used to fill in the input layer with a fixed length, the two-way gated recurrent network is used to extract information, and the attention mechanism is used to highlight the key information of the word vector. Finally, the GNU memory unit is transformed, and an improved BiGRU that can adapt to the recursive network structure is constructed. The proposed model is experimentally demonstrated on the SemEval-2014 Task 4 and SemEval-2017 Task 4 datasets. Experimental results show that the proposed model can effectively avoid the text sentiment analysis bias caused by spelling errors and prove the effectiveness of the improved AT-BiGRU model in terms of accuracy, loss rate, and iteration time.

scholarly journals TEXT SENTIMENT ANALYSIS BASED ON CNNS AND SVM

2019 ◽  
Vol 7 (6) ◽  
pp. 77-83 ◽  
Author(s):  
Dr. C. Arunabala ◽  
P. Jwalitha ◽  
Soniya Nuthalapati
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Sentiment Analysis ◽  
Expressive Power ◽  
Sentiment Classification ◽  
Experimental Results ◽  
Analysis Method ◽  
Mapping Functions ◽  
Generalization Ability ◽  
Text Sentiment Analysis

The traditional text sentiment analysis method is mainly based on machine learning. However, its dependence on emotion dictionary construction and artificial design and extraction features makes the generalization ability limited. In contrast, depth models have more powerful expressive power, and can learn complex mapping functions from data to affective semantics better. In this paper, a Convolution Neural Networks (CNNs) model combined with SVM text sentiment analysis is proposed. The experimental results show that the proposed method improves the accuracy of text sentiment classification effectively compared with traditional CNN, and confirms the effectiveness of sentiment analysis based on CNNs and SVM

scholarly journals A Sentiment Analysis Method Based on BLSTM and CNN Fusion

2018 ◽  
Vol 1087 (6) ◽  
pp. 062058
Author(s):  
Liedong Yin ◽  
Xia Ye ◽  
Junping Yao
Keyword(s):  
Sentiment Analysis ◽  
Analysis Method

A sentiment analysis method of objects by integrating sentiments from tweets

2019 ◽  
Vol 37 (6) ◽  
pp. 7251-7263 ◽  
Author(s):  
Huyen Trang Phan ◽  
Ngoc Thanh Nguyen ◽  
Van Cuong Tran ◽  
Dosam Hwang
Keyword(s):  
Sentiment Analysis ◽  
Analysis Method

An Interactive System with Facial Expression Recognition

2005 ◽  
Vol 9 (6) ◽  
pp. 637-642
Author(s):  
Yuyi Shang ◽  
◽  
Mie Sato ◽  
Masao Kasuga
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Interactive System ◽  
Expression Recognition ◽  
Interactive Communication ◽  
Motion Patterns ◽  
Voice Data ◽  
The Voice

To make communication between users and machines more comfortable, we focus on facial expressions and automatically classify them into 4 expression candidates: “joy,” “anger, ” “sadness,” and “surprise.” The classification uses features that correspond to expression-motion patterns, and then voice data is output based on classification results. When we output voice data, insufficiency in classification is taken into account. We choose the first and second expression candidates from classification results. To realize interactive communication between users and machines, information on these candidates is used when we access a voice database. The voice database contains voice data corresponding to emotions.

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