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Sentiment analysis on product reviews based on weighted word embeddings and deep neural networks
Abstract
Summary: Sentiment analysis is one of the major tasks of natural language processing, in which attitudes, thoughts, opinions, or judgments toward a particular subject has been extracted. Web is an unstructured and rich source of information containing many text documents with opinions and reviews. The recognition of sentiment can be helpful for individual decision makers, business organizations, and governments. In this article, we present a deep learning‐based approach to sentiment analysis on product reviews obtained from Twitter. The presented architecture combines TF‐IDF weighted Glove word embedding with CNN‐LSTM architecture. The CNN‐LSTM architecture consists of five layers, that is, weighted embedding layer, convolution layer (where, 1‐g, 2‐g, and 3‐g convolutions have been employed), max‐pooling layer, followed by LSTM, and dense layer. In the empirical analysis, the predictive performance of different word embedding schemes (ie, word2vec, fastText, GloVe, LDA2vec, and DOC2vec) with several weighting functions (ie, inverse document frequency, TF‐IDF, and smoothed inverse document frequency function) have been evaluated in conjunction with conventional deep neural network architectures. The empirical results indicate that the proposed deep learning architecture outperforms the conventional deep learning methods.
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Cites
Glove: Global Vectors for Word Representation
Pennington, J., Socher, R., & Manning, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1162 · Cited by 34,071
(2014) · Cited by 33,419
Efficient Estimation of Word Representations in Vector Space
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1301.3781 · Cited by 26,568
Convolutional Neural Networks for Sentence Classification
Kim, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1181 · Cited by 14,831
Elman, J. L. (1990) · Cognitive Science · doi:10.1207/s15516709cog1402_1 · Cited by 11,212
A unified architecture for natural language processing: deep neural networks with multitask learning
Collobert, R., & Weston, J. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390177 · Cited by 5,058
Natural Language Processing (almost) from Scratch
Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., & Kuksa, P. P. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1103.0398 · Cited by 4,962
Sentiment analysis algorithms and applications: A survey
Medhat, W., Hassan, A., & Korashy, H. (2014) · Ain Shams Engineering Journal · doi:10.1016/j.asej.2014.04.011 · Cited by 3,248
Deep learning for sentiment analysis: A survey
Zhang, L., Wang, S., & Liu, B. (2018) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1253 · Cited by 1,928
A survey of modern authorship attribution methods
Stamatatos, E. (2009) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.21001 · Cited by 1,176
Adaptive Recursive Neural Network for Target-dependent Twitter Sentiment Classification
Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., & Xu, K. (2014) · Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · doi:10.3115/v1/p14-2009 · Cited by 1,074
Generalization and network design strategies
LeCun, Y. (1989) · Cited by 885
Automatically Categorizing Written Texts by Author Gender
Koppel, M. (2002) · Literary and Linguistic Computing · doi:10.1093/llc/17.4.401 · Cited by 623
Sentiment analysis based on improved pre-trained word embeddings
Rezaeinia, S. M., Rahmani, R., Ghodsi, A., & Veisi, H. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.08.044 · Cited by 325
Onan, A., Korukoğlu, S., & Bulut, H. (2017) · Information Processing & Management · doi:10.1016/j.ipm.2017.02.008 · Cited by 305
Self-citationCuriskis, S. A., Drake, B., Osborn, T. R., & Kennedy, P. J. (2020) · Information Processing & Management · doi:10.1016/j.ipm.2019.04.002 · Cited by 304
Mining opinions from instructor evaluation reviews: a deep learning approach
Onan, A. (2020) · Computer Applications in Engineering Education · doi:10.1002/cae.22179 · Cited by 270
Onan, A. (2019) · IEEE Access · doi:10.1109/access.2019.2945911 · Cited by 257
Self-citationMastering Machine Learning With scikit-learn
Hackeling, G. (2014) · CERN Document Server (European Organization for Nuclear Research) · Cited by 238
Mixing Dirichlet Topic Models and Word Embeddings to Make lda2vecPreprint
Moody, C. E. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.02019 · Cited by 234
Representation learning for very short texts using weighted word embedding aggregation
De Boom, C., Van Canneyt, S., Demeester, T., & Dhoedt, B. (2016) · Pattern Recognition Letters · doi:10.1016/j.patrec.2016.06.012 · Cited by 199
Sentiment analysis: Bayesian Ensemble Learning
Fersini, E., Messina, E., & Pozzi, F. A. (2014) · Decision Support Systems · doi:10.1016/j.dss.2014.10.004 · Cited by 182
Deep learning for sentiment analysis
Rojas-Barahona, L. M. (2016) · Language and Linguistics Compass · doi:10.1111/lnc3.12228 · Cited by 141
An LSTM Approach to Short Text Sentiment Classification with Word Embeddings.
Wang, J., Liu, T., Luo, X., & Wang, L. (2018) · International Conference on Computational Linguistics · Cited by 113
Aydoğan, M., & Karcı, A. (2019) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2019.123288 · Cited by 61
Sentiment Analysis of Twitter Messages using Word2vec by Weighted Average
Djaballah, K. A., Boukhalfa, K., & Boussaïd, O. (2019) · International Conference on Social Networks Analysis, Management and Security (SNAMS) · doi:10.1109/snams.2019.8931827 · Cited by 31
Deep Learning Based Sentiment Analysis on Product Reviews on Twitter
Onan, A. (2019) · Communications in computer and information science · doi:10.1007/978-3-030-27355-2_6 · Cited by 25
Self-citationRecurrent neural networks for Turkish named entity recognition
Güngör, O., Üsküdarlı, S., & Güngör, T. (2018) · Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu.2018.8404788 · Cited by 11
Role of Data Properties on Sentiment Analysis of Texts via Convolutions
Çano, E., & Morisio, M. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-77703-0_34 · Cited by 5
Cited by
Sarker, I. H. (2022) · SN Computer Science · doi:10.1007/s42979-022-01043-x · Cited by 1,208
Systematic reviews in sentiment analysis: a tertiary study
Ligthart, A., Catal, C., & Tekinerdogan, B. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-021-09973-3 · Cited by 303
Impact of word embedding models on text analytics in deep learning environment: a review
Asudani, D. S., Nagwani, N. K., & Singh, P. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10419-1 · Cited by 274
A Review on Explainability in Multimodal Deep Neural Nets
Joshi, G., Walambe, R., & Kotecha, K. (2021) · IEEE Access · doi:10.1109/access.2021.3070212 · Cited by 204
Qu, Z., Liu, X., & Zheng, M. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3203791 · Cited by 140
Metadata
- Authors
- Onan, A.
- Venue
- Concurrency and Computation: Practice and Experience
- Publisher
- Wiley
- Published
- 2020-06-29
- Volume
- 33
- Issue
- 23
- ISSN
- 1532-0626 (ISSN-L)
- 1532-0634
- OpenAlex
- W3039503982
- Cited by
- 396
- References
- 29
- FWCI
- 27.37
- Type
- journal article
- Language
- en
- Open access
- closed
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