A Deep Learning based Customer Sentiment Analysis Model to Enhance Customer Retention and Loyalty in the Payment Industry

2020 ◽  
Author(s):  
George Wanganga ◽  
Yanzhen Qu
Keyword(s):  
Deep Learning ◽  
Sentiment Analysis ◽  
Customer Retention ◽  
Analysis Model

scholarly journals Sentiment Analysis of Film Reviews Based on BI-GRU +Attention+Capsule Fusion

2021 ◽  
Author(s):  
zhifei hu
Keyword(s):  
Deep Learning ◽  
Sentiment Analysis ◽  
Film Review ◽  
The Other ◽  
Learning Models ◽  
Analysis Model ◽  
Fusion Model ◽  
Data Set ◽  
Analysis Task ◽  
Film Reviews

In this paper, a sentiment analysis model based on the bi-directional GRU, Attention and Capusle fusion of BI-GRU+Attention+Capsule was designed and implemented based on the sentiment analysis task of the open film review data set IMDB, and combined with the bi-directional GRU, Attention and Capsule. It is compared with six deep learning models, such as LSTM, CNN, GRU, BI-GRU, CNN+GRU and GRU+CNN. The experimental results show that the accuracy of the BI-GRU model combined with Attention and Capusule is higher than the other six models, and the accuracy of the GRU+CNN model is higher than that of the CNN+GRU model, and the accuracy of the CNN+GRU model is higher than that of the CNN model. The accuracy of CNN model was successively higher than that of LSTM, BI-GRU and GRU model. The fusion model of BI-GRU +Attention+Capsule adopted in this paper has the highest accuracy among all the models. In conclusion, the fusion model of BI-GRU+Attention+Capsule effectively improves the accuracy of text sentiment classification.<br>

scholarly journals Twitter Sentiment Analysis Model

2021 ◽  
Vol 9 (VII) ◽  
pp. 296-302
Author(s):  
Pushkar Dubey
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Media ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Learning Approach ◽  
Analysis Model ◽  
Market Place ◽  
The Social

Social networks are the main resources to gather information about people’s opinion towards different topics as they spend hours daily on social media and share their opinion. Twitter is one of the social media that is gaining popularity. Twitter offers organizations a fast and effective way to analyze customers’ perspectives toward the critical to success in the market place. Developing a program for sentiment analysis is an approach to be used to computationally measure customers’ perceptions. .We use natural language processing and machine learning concepts to create a model for analysis . In this paper we are discussing how we can create a model for analysis of twittes which is trained by various nlp , machine learning and Deep learning Approach.

scholarly journals Performance Analysis of Hyperparameters on a Sentiment Analysis Model

2020 ◽  
Vol 10 (4) ◽  
pp. 6016-6020
Author(s):  
I. A. Kandhro ◽  
S. Z. Jumani ◽  
F. Ali ◽  
Z. U. Shaikh ◽  
M. A. Arain ◽  
...  
Keyword(s):  
Deep Learning ◽  
Performance Analysis ◽  
Sentiment Analysis ◽  
State Of The Art ◽  
Course Evaluation ◽  
Experimental Results ◽  
Activation Functions ◽  
Analysis Model ◽  
Proposed Model ◽  
Evaluation Dataset

This paper focuses on the performance analysis of hyperparameters of the Sentiment Analysis (SA) model of a course evaluation dataset. The performance was analyzed regarding hyperparameters such as activation, optimization, and regularization. In this paper, the activation functions used were adam, adagrad, nadam, adamax, and hard_sigmoid, the optimization functions were softmax, softplus, sigmoid, and relu, and the dropout values were 0.1, 0.2, 0.3, and 0.4. The results indicate that parameters adam and softmax with dropout value 2.0 are effective when compared to other combinations of the SA model. The experimental results reveal that the proposed model outperforms the state-of-the-art deep learning classifiers.

scholarly journals Sentiment Analysis of Film Reviews Based on BI-GRU +Attention+Capsule Fusion

2021 ◽  
Author(s):  
zhifei hu
Keyword(s):  
Deep Learning ◽  
Sentiment Analysis ◽  
Film Review ◽  
The Other ◽  
Learning Models ◽  
Analysis Model ◽  
Fusion Model ◽  
Data Set ◽  
Analysis Task ◽  
Film Reviews

In this paper, a sentiment analysis model based on the bi-directional GRU, Attention and Capusle fusion of BI-GRU+Attention+Capsule was designed and implemented based on the sentiment analysis task of the open film review data set IMDB, and combined with the bi-directional GRU, Attention and Capsule. It is compared with six deep learning models, such as LSTM, CNN, GRU, BI-GRU, CNN+GRU and GRU+CNN. The experimental results show that the accuracy of the BI-GRU model combined with Attention and Capusule is higher than the other six models, and the accuracy of the GRU+CNN model is higher than that of the CNN+GRU model, and the accuracy of the CNN+GRU model is higher than that of the CNN model. The accuracy of CNN model was successively higher than that of LSTM, BI-GRU and GRU model. The fusion model of BI-GRU +Attention+Capsule adopted in this paper has the highest accuracy among all the models. In conclusion, the fusion model of BI-GRU+Attention+Capsule effectively improves the accuracy of text sentiment classification.<br>

A Deep Neural Network Model for Cross-Domain Sentiment Analysis

2021 ◽  
Vol 12 (2) ◽  
pp. 1-16
Author(s):  
Suman Kumari ◽  
Basant Agarwal ◽  
Mamta Mittal
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Supervised Learning ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
State Of The Art ◽  
Analysis Model ◽  
Cross Domain ◽  
Unstructured Text

Sentiment analysis is used to detect the opinion/sentiment expressed from the unstructured text. Most of the existing state-of-the-art methods are based on supervised learning, and therefore, a labelled dataset is required to build the model, and it is very difficult task to obtain a labelled dataset for every domain. Cross-domain sentiment analysis is to develop a model which is trained on labelled dataset of one domain, and the performance is evaluated on another domain. The performance of such cross-domain sentiment analysis is still very limited due to presence of many domain-related terms, and the sentiment analysis is a domain-dependent problem in which words changes their polarity depending upon the domain. In addition, cross-domain sentiment analysis model suffers with the problem of large number of out-of-the-vocabulary (unseen words) words. In this paper, the authors propose a deep learning-based approach for cross-domain sentiment analysis. Experimental results show that the proposed approach improves the performance on the benchmark dataset.

scholarly journals Applying Deep Learning Techniques for Sentiment Analysis to Assess Sustainable Transport

2021 ◽  
Vol 13 (4) ◽  
pp. 2397
Author(s):  
Ainhoa Serna ◽  
Aitor Soroa ◽  
Rodrigo Agerri
Keyword(s):  
Deep Learning ◽  
Sentiment Analysis ◽  
Language Model ◽  
Sustainable Transport ◽  
Analysis Model ◽  
Classification Score ◽  
Learning Techniques ◽  
Textual Content ◽  
Fine Tune ◽  
Transport Domain

Users voluntarily generate large amounts of textual content by expressing their opinions, in social media and specialized portals, on every possible issue, including transport and sustainability. In this work we have leveraged such User Generated Content to obtain a high accuracy sentiment analysis model which automatically analyses the negative and positive opinions expressed in the transport domain. In order to develop such model, we have semiautomatically generated an annotated corpus of opinions about transport, which has then been used to fine-tune a large pretrained language model based on recent deep learning techniques. Our empirical results demonstrate the robustness of our approach, which can be applied to automatically process massive amounts of opinions about transport. We believe that our method can help to complement data from official statistics and traditional surveys about transport sustainability. Finally, apart from the model and annotated dataset, we also provide a transport classification score with respect to the sustainability of the transport types found in the use case dataset.

Deep Learning for text in limted data settings

2020 ◽  
Author(s):  
Pathikkumar Patel ◽  
Bhargav Lad ◽  
Jinan Fiaidhi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Text Classification ◽  
State Of The Art ◽  
Time Series Forecasting ◽  
Text Data ◽  
Performance Levels

During the last few years, RNN models have been extensively used and they have proven to be better for sequence and text data. RNNs have achieved state-of-the-art performance levels in several applications such as text classification, sequence to sequence modelling and time series forecasting. In this article we will review different Machine Learning and Deep Learning based approaches for text data and look at the results obtained from these methods. This work also explores the use of transfer learning in NLP and how it affects the performance of models on a specific application of sentiment analysis.

Improving Sentiment Analysis using Hybrid Deep Learning Model

2020 ◽  
Vol 13 (4) ◽  
pp. 627-640 ◽  
Author(s):  
Avinash Chandra Pandey ◽  
Dharmveer Singh Rajpoot
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Computational Cost ◽  
Extraction Process ◽  
Learning Model ◽  
Sentiment Classification ◽  
Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

Sign in / Sign up

Export Citation Format

Share Document