scholarly journals Fuzzy Cloud Evaluation of Service Quality Based on DP-FastText

2021 ◽  
Vol 20 ◽  
pp. 149-167
Author(s):  
Huanzhuo Ye ◽  
Yuan Li
Keyword(s):  
Service Quality ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Quality Evaluation ◽  
Classification Model ◽  
Data Preparation ◽  
Model Framework ◽  
Automatic Data ◽  
Fine Grained ◽  
Service Quality Evaluation

This study proposes a service quality evaluation model framework which integrates automatic data acquisition, intelligent data processing and real-time data analysis with online comment data as data sources by introducing natural language processing technology based on management methods to break the traditional idea of over-reliance on human resources for service quality evaluation. The framework is mainly divided into text data preparation, fine-grained sentiment analysis and fuzzy cloud evaluation models. Data preparation module is responsible for preparing the initial data, and the fine-grained sentiment analysis module is responsible for pre-training a fine-grained sentiment classification model. The fuzzy cloud evaluation module uses the data obtained from the first two modules to evaluate service quality. By applying the model into catering industry, the feasibility of the model is proved and individuality, efficiency, dynamicity and intelligence of the model give it more advantage in the practice of service quality evaluation

Learning emotional word embeddings for sentiment analysis

2021 ◽  
pp. 1-13
Author(s):  
Qingtian Zeng ◽  
Xishi Zhao ◽  
Xiaohui Hu ◽  
Hua Duan ◽  
Zhongying Zhao ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
State Of The Art ◽  
Research Problem ◽  
Emotional Word ◽  
Classification Model ◽  
Data Sets ◽  
Word Embeddings ◽  
Real World Data ◽  
Text Documents

Word embeddings have been successfully applied in many natural language processing tasks due to its their effectiveness. However, the state-of-the-art algorithms for learning word representations from large amounts of text documents ignore emotional information, which is a significant research problem that must be addressed. To solve the above problem, we propose an emotional word embedding (EWE) model for sentiment analysis in this paper. This method first applies pre-trained word vectors to represent document features using two different linear weighting methods. Then, the resulting document vectors are input to a classification model and used to train a text sentiment classifier, which is based on a neural network. In this way, the emotional polarity of the text is propagated into the word vectors. The experimental results on three kinds of real-world data sets demonstrate that the proposed EWE model achieves superior performances on text sentiment prediction, text similarity calculation, and word emotional expression tasks compared to other state-of-the-art models.

scholarly journals A dynamic multi-dimension trust model for information service quality evaluation

2021 ◽  
Vol 187 ◽  
pp. 601-606
Author(s):  
Zhicheng Xu ◽  
Xiang Li ◽  
Wanyin Xiong ◽  
Qixiao Lin ◽  
Jian Mao
Keyword(s):  
Service Quality ◽  
Quality Evaluation ◽  
Information Service ◽  
Trust Model ◽  
Service Quality Evaluation
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