Sentiment Analysis Using Deep Learning Techniques: A Review

The World Wide Web such as social networks, forums, review sites and blogs generate enormous heaps of data in the form of users views, emotions, opinions and arguments about different social events, products, brands, and politics. Sentiments of users that are expressed on the web has great influence on the readers, product vendors and politicians. The unstructured form of data from the social media is needed to be analyzed and well-structured and for this purpose, sentiment analysis has recognized significant attention. Sentiment analysis is referred as text organization that is used to classify the expressed mind-set or feelings in different manners such as negative, positive, favorable, unfavorable, thumbs up, thumbs down, etc. The challenge for sentiment analysis is lack of sufficient labeled data in the field of Natural Language Processing (NLP). And to solve this issue, the sentiment analysis and deep learning techniques have been merged because deep learning models are effective due to their automatic learning capability. This Review Paper highlights latest studies regarding the implementation of deep learning models such as deep neural networks, convolutional neural networks and many more for solving different problems of sentiment analysis such as sentiment classification, cross lingual problems, textual and visual analysis and product review analysis, etc.

[1]  Hidekazu Yanagimoto,et al.  Document similarity estimation for sentiment analysis using neural network , 2013, 2013 IEEE/ACIS 12th International Conference on Computer and Information Science (ICIS).

[2]  Bo Xu,et al.  Recursive Deep Learning for Sentiment Analysis over Social Data , 2014, 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT).

[3]  Alessandro Moschitti,et al.  Twitter Sentiment Analysis with Deep Convolutional Neural Networks , 2015, SIGIR.

[4]  Andrew Y. Ng,et al.  Parsing Natural Scenes and Natural Language with Recursive Neural Networks , 2011, ICML.

[5]  Jiajun Zhang,et al.  Deep Neural Networks in Machine Translation: An Overview , 2015, IEEE Intelligent Systems.

[6]  Peerapon Vateekul,et al.  A study of sentiment analysis using deep learning techniques on Thai Twitter data , 2016, 2016 13th International Joint Conference on Computer Science and Software Engineering (JCSSE).

[7]  Andreas M. Kaplan,et al.  An Empirical Analysis of Attitudinal and Behavioral Reactions Toward the Abandonment of Unprofitable Customer Relationships , 2010 .

[8]  Taghi M. Khoshgoftaar,et al.  Cross-Domain Sentiment Analysis: An Empirical Investigation , 2016, 2016 IEEE 17th International Conference on Information Reuse and Integration (IRI).

[9]  Christopher D. Manning,et al.  Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks , 2015, ACL.

[10]  Haizhou Li,et al.  Exemplar-based unit selection for voice conversion utilizing temporal information , 2013, INTERSPEECH.

[11]  Xiaolong Wang,et al.  Active deep learning method for semi-supervised sentiment classification , 2013, Neurocomputing.

[12]  Tao Chen,et al.  Learning User and Product Distributed Representations Using a Sequence Model for Sentiment Analysis , 2016, IEEE Computational Intelligence Magazine.

[13]  Michael S. Lew,et al.  Deep learning for visual understanding: A review , 2016, Neurocomputing.

[14]  Tiranee Achalakul,et al.  Deep Belief Networks with Feature Selection for Sentiment Classification , 2016, 2016 7th International Conference on Intelligent Systems, Modelling and Simulation (ISMS).

[15]  Jeffrey Dean,et al.  Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.

[16]  Lijun Liu,et al.  Sentiment Analysis Using Convolutional Neural Network , 2015, 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing.

[17]  Thierry Denoeux,et al.  Integrated Uncertainty in Knowledge Modelling and Decision Making , 2015, Lecture Notes in Computer Science.

[18]  Chen Yuda,et al.  Research on Chinese Micro-Blog Sentiment Analysis Based on Deep Learning , 2015, 2015 8th International Symposium on Computational Intelligence and Design (ISCID).

[19]  Hirosato Seki.  Integrated Uncertainty in Knowledge Modelling and Decision Making , 2019, Lecture Notes in Computer Science.

[20]  Sébastien Rebecchi,et al.  An Introduction to Deep Learning , 2011, ESANN.

[21]  Samy Bengio,et al.  Guest Editors' Introduction: Special Section on Learning Deep Architectures , 2013, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Jiebo Luo,et al.  Joint Visual-Textual Sentiment Analysis with Deep Neural Networks , 2015, ACM Multimedia.

[23]  Min-Yuh Day,et al.  Deep learning for financial sentiment analysis on finance news providers , 2016, 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).

[24]  Kumar Ravi,et al.  A novel deep learning architecture for sentiment classification , 2016, 2016 3rd International Conference on Recent Advances in Information Technology (RAIT).

[25]  Hsinchun Chen,et al.  Identifying Top Sellers In Underground Economy Using Deep Learning-Based Sentiment Analysis , 2014, 2014 IEEE Joint Intelligence and Security Informatics Conference.

[26]  Hazem M. Hajj,et al.  Deep Learning Models for Sentiment Analysis in Arabic , 2015, ANLP@ACL.

[27]  Christopher Potts,et al.  Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank , 2013, EMNLP.

[28]  Yong Zhang,et al.  Sentiment classification using Comprehensive Attention Recurrent models , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).

[29]  Roman Senkerik,et al.  Automation Control Theory Perspectives in Intelligent Systems - Proceedings of the 5th Computer Science On-line Conference 2016 (CSOC2016), Vol 3 , 2016, CSOC.

[30]  Brian Kingsbury,et al.  New types of deep neural network learning for speech recognition and related applications: an overview , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[31]  Phil Blunsom,et al.  A Convolutional Neural Network for Modelling Sentences , 2014, ACL.

[32]  Jeffrey Dean,et al.  Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.

[33]  Kumar Ravi,et al.  Sentiment classification of Hinglish text , 2016, 2016 3rd International Conference on Recent Advances in Information Technology (RAIT).

[34]  Cheng Li,et al.  Affective-feature-based sentiment analysis using SVM classifier , 2016, 2016 IEEE 20th International Conference on Computer Supported Cooperative Work in Design (CSCWD).

[35]  Navdeep Jaitly,et al.  Hybrid speech recognition with Deep Bidirectional LSTM , 2013, 2013 IEEE Workshop on Automatic Speech Recognition and Understanding.

[36]  Ashwini V. Yeole,et al.  Opinion mining for emotions determination , 2015, 2015 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS).

[37]  Yoon Kim,et al.  Convolutional Neural Networks for Sentence Classification , 2014, EMNLP.

[38]  M. Ali Akcayol,et al.  A comprehensive survey for sentiment analysis tasks using machine learning techniques , 2016, 2016 International Symposium on INnovations in Intelligent SysTems and Applications (INISTA).

[39]  Yanqing Zhang,et al.  Visual Sentiment Analysis for Social Images Using Transfer Learning Approach , 2016, 2016 IEEE International Conferences on Big Data and Cloud Computing (BDCloud), Social Computing and Networking (SocialCom), Sustainable Computing and Communications (SustainCom) (BDCloud-SocialCom-SustainCom).

[40]  Cícero Nogueira dos Santos,et al.  Deep Convolutional Neural Networks for Sentiment Analysis of Short Texts , 2014, COLING.

[41]  Alberto Del Bimbo,et al.  A multimodal feature learning approach for sentiment analysis of social network multimedia , 2016, Multimedia Tools and Applications.

[42]  Jaspreet Singh,et al.  A review of sentiment analysis techniques for opinionated web text , 2016, CSI Transactions on ICT.