Hybrid convolutional bidirectional recurrent neural network based sentiment analysis on movie reviews

[1]  Bo Pang,et al.  A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts , 2004, ACL.

[2]  Emre Çomak,et al.  A new training method for support vector machines: Clustering k-NN support vector machines , 2008, Expert Syst. Appl..

[3]  Xue Bai,et al.  Predicting consumer sentiments from online text , 2011, Decis. Support Syst..

[4]  Abien Fred Agarap.  A Neural Network Architecture Combining Gated Recurrent Unit (GRU) and Support Vector Machine (SVM) for Intrusion Detection in Network Traffic Data , 2017, ICMLC.

[5]  Christopher Potts,et al.  Learning Word Vectors for Sentiment Analysis , 2011, ACL.

[6]  Marek Kozlowski,et al.  Word Sense Induction with Closed Frequent Termsets , 2017, Comput. Intell..

[7]  Mutasem Sh. Alkhasawneh,et al.  Hybrid Cascade Forward Neural Network with Elman Neural Network for Disease Prediction , 2019, Arabian Journal for Science and Engineering.

[8]  Tong Zhang,et al.  Supervised and Semi-Supervised Text Categorization using LSTM for Region Embeddings , 2016, ICML.

[9]  Z R Li,et al.  Machine learning approaches for predicting compounds that interact with therapeutic and ADMET related proteins. , 2007, Journal of pharmaceutical sciences.

[10]  Yijia Zhang,et al.  Extracting drug-drug interactions with hybrid bidirectional gated recurrent unit and graph convolutional network , 2019, J. Biomed. Informatics.

[11]  Xiong Luo,et al.  Attention-Based Relation Extraction With Bidirectional Gated Recurrent Unit and Highway Network in the Analysis of Geological Data , 2018, IEEE Access.

[12]  Hao Zhou,et al.  A gated recurrent unit neural networks based wind speed error correction model for short-term wind power forecasting , 2019, Neurocomputing.

[13]  H. Aizenstein,et al.  Studying depression using imaging and machine learning methods , 2015, NeuroImage: Clinical.

[14]  Spyros Kotoulas,et al.  Medical Text Classification using Convolutional Neural Networks , 2017, Studies in health technology and informatics.

[15]  Yu-N Cheah,et al.  Aspect extraction in sentiment analysis: comparative analysis and survey , 2016, Artificial Intelligence Review.

[16]  Tao Chen,et al.  Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN , 2017, Expert Syst. Appl..

[17]  Erik Cambria,et al.  Sentic patterns: Dependency-based rules for concept-level sentiment analysis , 2014, Knowl. Based Syst..

[18]  Craig MacDonald,et al.  Using word embeddings in Twitter election classification , 2016, Information Retrieval Journal.

[19]  Abhishek Verma,et al.  Deep CNN-LSTM with combined kernels from multiple branches for IMDb review sentiment analysis , 2017, 2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON).

[20]  Muhammad Zubair Asghar,et al.  Detection and classification of social media-based extremist affiliations using sentiment analysis techniques , 2019, Human-centric Computing and Information Sciences.

[21]  Yuzhen Lu,et al.  Simplified gating in long short-term memory (LSTM) recurrent neural networks , 2017, 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS).

[22]  Mohsen Guizani,et al.  Machine learning in the Internet of Things: Designed techniques for smart cities , 2019, Future Gener. Comput. Syst..

[23]  Erik Cambria,et al.  Sentiment and Sarcasm Classification With Multitask Learning , 2019, IEEE Intelligent Systems.

[24]  Yongfeng Huang,et al.  Knowledge-enhanced neural networks for sentiment analysis of Chinese reviews , 2019, Neurocomputing.

[25]  Rui Li,et al.  Sentiment classification with adversarial learning and attention mechanism , 2020, Comput. Intell..

[26]  Yuan Liu,et al.  Financial quantitative investment using convolutional neural network and deep learning technology , 2020, Neurocomputing.

[27]  Ausif Mahmood,et al.  Deep Learning approach for sentiment analysis of short texts , 2017, 2017 3rd International Conference on Control, Automation and Robotics (ICCAR).

[28]  Fernando de la Prieta,et al.  Sentiment Analysis Based on Deep Learning: A Comparative Study , 2020, Electronics.

[29]  M. R,et al.  Stance detection and mobile app recommendation discourse on tweets , 2019 .

[30]  J. Ramon,et al.  Machine learning techniques to examine large patient databases. , 2009, Best practice & research. Clinical anaesthesiology.

[31]  Alex Alves Freitas,et al.  An extensive evaluation of seven machine learning methods for rainfall prediction in weather derivatives , 2017, Expert Syst. Appl..

[32]  Dong Ling Tong,et al.  Genetic Algorithm-Neural Network (GANN): a study of neural network activation functions and depth of genetic algorithm search applied to feature selection , 2010, Int. J. Mach. Learn. Cybern..

[33]  Yoav Goldberg,et al.  A Primer on Neural Network Models for Natural Language Processing , 2015, J. Artif. Intell. Res..

[34]  Fazeel Abid,et al.  Social media sentiment analysis through parallel dilated convolutional neural network for smart city applications , 2020, Comput. Commun..

[35]  Ankit Sharma,et al.  Sentiment Analysis of Movie Review Using Supervised Machine Learning Techniques , 2018 .

[36]  El-Houssine Bouyakhf,et al.  Access and Sharing Contents Through the Social Network: A POMDP Approach , 2017, Comput. Intell..

[37]  M. Botvinick.  Hierarchical reinforcement learning and decision making , 2012, Current Opinion in Neurobiology.

[38]  Yi Yang,et al.  A User Profile Modeling Method Based on Word2Vec , 2017, 2017 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C).

[39]  Hideaki Aoyama,et al.  Enhanced news sentiment analysis using deep learning methods , 2019, Journal of Computational Social Science.

[40]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[41]  Habin Lee,et al.  Data properties and the performance of sentiment classification for electronic commerce applications , 2017, Inf. Syst. Frontiers.

[42]  Ausif Mahmood,et al.  Deep learning for sentence classification , 2017, 2017 IEEE Long Island Systems, Applications and Technology Conference (LISAT).

[43]  Santanu Kumar Rath,et al.  Document-level sentiment classification using hybrid machine learning approach , 2017, Knowledge and Information Systems.

[44]  Mike Thelwall,et al.  Sentiment Analysis Is a Big Suitcase , 2017, IEEE Intelligent Systems.

[45]  Wei Gao,et al.  From classification to quantification in tweet sentiment analysis , 2016, Social Network Analysis and Mining.

[46]  Marie-Francine Moens,et al.  A survey on the application of recurrent neural networks to statistical language modeling , 2015, Comput. Speech Lang..

[47]  Jun Wang,et al.  Learning text representation using recurrent convolutional neural network with highway layers , 2016, SIGIR 2016.

[48]  Taghi M. Khoshgoftaar,et al.  Improving deep neural network design with new text data representations , 2017, Journal of Big Data.

[49]  Yulan He,et al.  Convolution-Based Neural Attention With Applications to Sentiment Classification , 2019, IEEE Access.

[50]  Björn W. Schuller,et al.  Contextual Bidirectional Long Short-Term Memory Recurrent Neural Network Language Models: A Generative Approach to Sentiment Analysis , 2017, EACL.

[51]  Sebastian Thrun,et al.  Text Classification from Labeled and Unlabeled Documents using EM , 2000, Machine Learning.

[52]  Lin Li,et al.  How textual quality of online reviews affect classification performance: a case of deep learning sentiment analysis , 2018, Neural Computing and Applications.

[53]  Yu Zhao,et al.  Applying Deep Bidirectional LSTM and Mixture Density Network for Basketball Trajectory Prediction , 2017, ArXiv.

[54]  Omar Alqaryouti,et al.  Aspect-based sentiment analysis using smart government review data , 2020, Applied Computing and Informatics.

[55]  Jingzhi Guo,et al.  Chinese semantic document classification based on strategies of semantic similarity computation and correlation analysis , 2020, J. Web Semant..

[56]  S. Sivakumar,et al.  Multiservice product comparison system with improved reliability in big data broadcasting , 2017, 2017 Third International Conference on Science Technology Engineering & Management (ICONSTEM).

[57]  WuYunfang,et al.  SemEval-2010 task 18 , 2013 .

[58]  Amit Kumar Yadav,et al.  Solar radiation prediction using Artificial Neural Network techniques: A review , 2014 .

[59]  R. Rajalakshmi,et al.  Comparative Evaluation of Various Feature Weighting Methods on Movie Reviews , 2018, Advances in Intelligent Systems and Computing.

[60]  Lea Tien Tay,et al.  A Hybrid Intelligent System Integrating the Cascade Forward Neural Network with Elman Neural Network , 2018 .

[61]  Jun Li,et al.  Automatic synonym extraction using Word2Vec and spectral clustering , 2017, 2017 36th Chinese Control Conference (CCC).

[62]  Erik Cambria,et al.  Word Polarity Disambiguation Using Bayesian Model and Opinion-Level Features , 2014, Cognitive Computation.

[63]  Daniel Westreich,et al.  Propensity score estimation: neural networks, support vector machines, decision trees (CART), and meta-classifiers as alternatives to logistic regression. , 2010, Journal of clinical epidemiology.

[64]  Ashraful Islam,et al.  Intrusion Detection System for the Internet of Things Based on Blockchain and Multi-Agent Systems , 2020, Electronics.

[65]  R Muthusami,et al.  Stance detection and mobile app recommendation discourse on tweets , 2019, Comput. Intell..

[66]  Howon Kim,et al.  Classification performance using gated recurrent unit recurrent neural network on energy disaggregation , 2016, 2016 International Conference on Machine Learning and Cybernetics (ICMLC).