Improving the review classification of Google apps using combined feature embedding and deep convolutional neural network model

[1]  Anureet Kaur,et al.  Systematic literature review of mobile application development and testing effort estimation , 2018, J. King Saud Univ. Comput. Inf. Sci..

[2]  Huanlai Xing,et al.  A federated learning system with enhanced feature extraction for human activity recognition , 2021, Knowl. Based Syst..

[3]  Rong Qu,et al.  RNTS: Robust Neural Temporal Search for Time Series Classification , 2021, 2021 International Joint Conference on Neural Networks (IJCNN).

[4]  Michele Nappi,et al.  Discrepancy detection between actual user reviews and numeric ratings of Google App store using deep learning , 2021, Expert Syst. Appl..

[5]  Aniello Castiglione,et al.  COVID-19: Automatic Detection of the Novel Coronavirus Disease From CT Images Using an Optimized Convolutional Neural Network , 2021, IEEE Transactions on Industrial Informatics.

[6]  Huanlai Xing,et al.  RTFN: A Robust Temporal Feature Network for Time Series Classification , 2020, Inf. Sci..

[7]  Masayoshi Aritsugi,et al.  Extracting Arguments Based on User Decisions in App Reviews , 2021, IEEE Access.

[8]  Michele Nappi,et al.  Emotion Recognition by Textual Tweets Classification Using Voting Classifier (LR-SGD) , 2021, IEEE Access.

[9]  Diego Reforgiato Recupero,et al.  Ensembling Classical Machine Learning and Deep Learning Approaches for Morbidity Identification From Clinical Notes , 2021, IEEE Access.

[10]  Gyu Sang Choi,et al.  Aggression detection through deep neural model on Twitter , 2021, Future Gener. Comput. Syst..

[11]  Diego Reforgiato Recupero,et al.  TF-IDF vs Word Embeddings for Morbidity Identification in Clinical Notes: An Initial Study , 2021, SmartPhil@IUI.

[12]  Gyu Sang Choi,et al.  Extensive hotel reviews classification using long short term memory , 2020, Journal of Ambient Intelligence and Humanized Computing.

[13]  A. Hassanien,et al.  Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media , 2020, Applied Soft Computing.

[14]  Leila Posenato Garcia,et al.  Infodemic: excess quantity to the detriment of quality of information about COVID-19. , 2020, Epidemiologia e servicos de saude : revista do Sistema Unico de Saude do Brasil.

[15]  Gyu Sang Choi,et al.  Predicting numeric ratings for Google apps using text features and ensemble learning , 2020, ETRI Journal.

[16]  Shuai Zhang,et al.  Rating prediction via generative convolutional neural networks based regression , 2020, Pattern Recognit. Lett..

[17]  Kyoung-jae Kim,et al.  Impact of Word Embedding Methods on Performance of Sentiment Analysis with Machine Learning Techniques , 2020 .

[18]  Xiaowei Xu,et al.  Predicting the Helpfulness of Online Restaurant Reviews Using Different Machine Learning Algorithms: A Case Study of Yelp , 2019, Sustainability.

[19]  M. Mohammed Thaha,et al.  Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images , 2019, Journal of Medical Systems.

[20]  Eréndira Rendón Lara,et al.  Addressing the Big Data Multi-class Imbalance Problem with Oversampling and Deep Learning Neural Networks , 2019, IbPRIA.

[21]  Oladimeji Farri,et al.  Comparative effectiveness of convolutional neural network (CNN) and recurrent neural network (RNN) architectures for radiology text report classification , 2019, Artif. Intell. Medicine.

[22]  Chih-Fong Tsai,et al.  Under-sampling class imbalanced datasets by combining clustering analysis and instance selection , 2019, Inf. Sci..

[23]  V. E. Adeyemo,et al.  SOFTWARE DEFECT PREDICTION: ANALYSIS OF CLASS IMBALANCE AND PERFORMANCE STABILITY , 2019 .

[24]  Zhendong Niu,et al.  Tensor factorization method based on review text semantic similarity for rating prediction , 2018, Expert Syst. Appl..

[25]  Li-xia Luo.  Network text sentiment analysis method combining LDA text representation and GRU-CNN , 2018, Personal and Ubiquitous Computing.

[26]  Shahzad Qaiser,et al.  Text Mining: Use of TF-IDF to Examine the Relevance of Words to Documents , 2018, International Journal of Computer Applications.

[27]  Shuai Zhang,et al.  A novel ensemble method for credit scoring: Adaption of different imbalance ratios , 2018, Expert Syst. Appl..

[28]  Vimalraj S Spelmen,et al.  A Review on Handling Imbalanced Data , 2018, 2018 International Conference on Current Trends towards Converging Technologies (ICCTCT).

[29]  Abdelouahed Gherbi,et al.  Fake Reviews Detection on Movie Reviews through Sentiment Analysis Using Supervised Learning Techniques , 2018 .

[30]  Haitao Huang,et al.  Abstractive text summarization using LSTM-CNN based deep learning , 2018, Multimedia Tools and Applications.

[31]  Adiwijaya,et al.  Handling imbalanced data in churn prediction using ADASYN and backpropagation algorithm , 2017, 2017 3rd International Conference on Science in Information Technology (ICSITech).

[32]  Qiang Wu,et al.  Retinal Microaneurysm Detection Using Clinical Report Guided Multi-sieving CNN , 2017, MICCAI.

[33]  Tareq Abed Mohammed,et al.  Understanding of a convolutional neural network , 2017, 2017 International Conference on Engineering and Technology (ICET).

[34]  J. Fernando Sánchez-Rada,et al.  Enhancing deep learning sentiment analysis with ensemble techniques in social applications , 2020 .

[35]  Harald C. Gall,et al.  Analyzing reviews and code of mobile apps for better release planning , 2017, 2017 IEEE 24th International Conference on Software Analysis, Evolution and Reengineering (SANER).

[36]  Matthijs Douze,et al.  FastText.zip: Compressing text classification models , 2016, ArXiv.

[37]  Dagmar Monett,et al.  Predicting star ratings based on annotated reviews of mobile apps , 2016, 2016 Federated Conference on Computer Science and Information Systems (FedCSIS).

[38]  Walid Maalej,et al.  On the automatic classification of app reviews , 2016, Requirements Engineering.

[39]  Gabriele Bavota,et al.  Release Planning of Mobile Apps Based on User Reviews , 2016, 2016 IEEE/ACM 38th International Conference on Software Engineering (ICSE).

[40]  Navneet Kaur,et al.  Opinion mining and sentiment analysis , 2016, 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom).

[41]  Michael Luca.  Reviews, Reputation, and Revenue: The Case of Yelp.Com , 2016 .

[42]  Victor Alves,et al.  Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images , 2016, IEEE Transactions on Medical Imaging.

[43]  Harald C. Gall,et al.  How can i improve my app? Classifying user reviews for software maintenance and evolution , 2015, 2015 IEEE International Conference on Software Maintenance and Evolution (ICSME).

[44]  David Lo,et al.  What are the characteristics of high-rated apps? A case study on free Android Applications , 2015, 2015 IEEE International Conference on Software Maintenance and Evolution (ICSME).

[45]  Hayit Greenspan,et al.  Chest pathology detection using deep learning with non-medical training , 2015, 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI).

[46]  Jeffrey Pennington,et al.  GloVe: Global Vectors for Word Representation , 2014, EMNLP.

[47]  Bo Jiang,et al.  Machine Learning and Lexicon Based Methods for Sentiment Classification: A Survey , 2014, 2014 11th Web Information System and Application Conference.

[48]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[49]  Caroline Petitjean,et al.  Classification of Endomicroscopic Images of the Lung Based on Random Subwindows and Extra-Trees , 2012, IEEE Transactions on Biomedical Engineering.

[50]  Murat Korkmaz,et al.  THE IMPORTANCE OF LOGISTIC REGRESSION IMPLEMENTATIONS IN THE TURKISH LIVESTOCK SECTOR AND LOGISTIC REGRESSION IMPLEMENTATIONS/FIELDS , 2012 .

[51]  Léon Bottou,et al.  Stochastic Gradient Descent Tricks , 2012, Neural Networks: Tricks of the Trade.

[52]  Bing Liu,et al.  Sentiment Analysis and Subjectivity , 2010, Handbook of Natural Language Processing.

[53]  Haibo He,et al.  ADASYN: Adaptive synthetic sampling approach for imbalanced learning , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[54]  Robert P. Sheridan,et al.  Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling , 2003, J. Chem. Inf. Comput. Sci..

[55]  Nitesh V. Chawla,et al.  SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..

[56]  J. Friedman.  Greedy function approximation: A gradient boosting machine. , 2001 .