The week's pick
Random Articles
Reseach Article
Sentiment Classification of Tweets in Twitter using CNN and Dropouts in RNN
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 175 - Number 33 |
| Year of Publication: 2020 |
| Authors: Poornima A., K. Sathya Priya |
10.5120/ijca2020920681
|
Poornima A., K. Sathya Priya . Sentiment Classification of Tweets in Twitter using CNN and Dropouts in RNN. International Journal of Computer Applications. 175, 33 ( Nov 2020), 1-5. DOI=10.5120/ijca2020920681
Abstract
Sentimental analysis is the computational study of people’s opinion, attitudes and emotions toward entities, individuals, issues, events or topic. A lot of research has been done to improve the accuracy of sentiment analysis, varying from simple linear models to more complex deep neural network models. Recently, Deep learning has shown great success in the field of sentiment analysis and is considered as the state-of-the- art model. The twitter data imposes many challenges, due to its complex structure, various dialects, in addition to the lack of its resources. Although, the recent Deep learning models have improved the accuracy of the twitter sentiment analysis, there is still more chance for improvement. This encouraged to explore different deep learning hybrid models that have not been applied to twitter data, in order to improve the twitter sentiment analysis accuracy. The objective of this paper is to improve the accuracy of sentiment analysis of twitter data by implementing a hybrid model of CNN-RNN techniques and by introducing dropout in the hybrid model and also compare the performance of the proposed method with existingmodels.
References
- Yaser Maher Wazery, Hager Saleh Mohammed, EssamHalimHoussein, ”Twitter Sentiment Analysis using Deep Neural Network”, 14th Interna - tional Computer Engineering Conference (ICENCO), Cairo, Egypt, 2018, pp. 177-182.
- A. Ilmania, Abdurrahman, S. Cahyawijayaand A. Purwarianti, ”Aspect Detection and Sentiment Classification Using Deep Neural Network for Indonesian Aspect-Based Sentiment Analysis,” 2018 International Conference on Asian Language Processing (IALP), Bandung, Indonesia, 2018, pp.62-67
- Merin Thomas, Latha C.A, “Sentimental analysis using recurrent neural network”, International Journal of Engineering and Technology, Vol.7, No 2.27,2018.
- Ganda, Raouf, Mahmood, Ausif, “Sentiment Analysis With Re- Current Neural Network And Unsupervised Neural Language Model”, 42nd International Conference on Acoustics, Speech and Signal Processing (ICASSP)2017.
- P.Zhang,H.Zhu,T.XiongandY.Yang,”Co-attentionNetworkandLow- rank Bilinear Pooling for Aspect Based Sentiment Analysis,” ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, United Kingdom, 2019, pp. 6725- 6729.
- Y. Zhang, M. J. Er, R. Venkatesan, N. Wang and M. Pratama, ”Sentiment classification using Comprehensive Attention Recurrent models,” 2016 International Joint Conference on Neural Networks (IJCNN), Vancouver, BC, 2016, pp.1562-1569.
- F. Yang, C. Du, L. Huang, “Ensemble Sentiment Analysis Method based on R-CnnAnd C-Rnn With Fusion Gate”, International Journal Of Computers Communications Control Issn1841-9836, E-Issn 1841-9844, 14(2), 272-285, April2019.
- C. Du, L. Huang, “Text Classification Research with Attention-based Recurrent Neural Networks”, International Journal Of Com- Puters Communications Control Issn 1841-9836, 13(1), 50- 61, February2018.
- AsadAbdia, SitiMariyamShamsuddina, ShafaatunnurHasana, JalilPiranb, “Deep learning-based sentiment classification of evaluative text based on Multi-feature fusion”, Information Processing and Management, Vol. 56, Pg. 1245-1259,2019.
- MahaHeikal, Marwan Torki, Nagwa El-Makky, “Sentiment Analysis of Arabic Tweets using Deep Learning”, The 4th International Conference on Arabic Computational Linguistics (ACLing 2018), November 17-19 2018.
- G. Xu, Y. Meng, X. Qiu, Z. Yu and X. Wu, ”Sentiment Analysis of Comment Texts Based on BiLSTM,” in IEEE Access, vol. 7, pp. 51522- 51532,2019.
- Li, Liu, Zhang, “An Improved Approach for Text Sentiment Clas- sification Based on a Deep Neural Network via a Sentiment Attention Mechanism”, Vol.11,2019.
- Akhtar, Md. Shad, AyushKumar, AsifEkbal and PushpakBhat- tacharyya. “A Hybrid Deep Learning Architecture for Sentiment Anal- ysis.” COLING(2016).
- A. Cahyadi and M. L. Khodra, ”Aspect-Based Sentiment Analysis Using Convolutional Neural Network and Bidirectional Long Short-Term Memory,” 2018 5th International Conference on Advanced Informatics: Concept Theory and Applications (ICAICTA), Krabi, 2018, pp. 124-129, doi:10.1109/ICAICTA.2018.8541300.
- Zhang, Ziqi and Robinson, David and Tepper, Jonathan, “Detecting Hate Speech on Twitter Using a Convolution-GRU Based Deep Neural Network”, Springer International Publishing, 2018, pp. 745—760, isbn: 978-3-319-93417-4.
- D. Ekawati and M. L. Khodra, ”Aspect-based sentiment analysis for indonesian restaurant reviews,” in ICAICTA,2017.
- X. Ding, B. Liu and P. S. Yu, ”A Holistic Lexicon-Based Approach to Opinion Mining,” in Proceedings of the Conference on Web Search and Web Data Mining (WSDM),2008.
- J. Pennington, R. Socher and C. D. Manning, ”GloVe: Global Vectors for Word Representation,” in The 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP),2014.
- Z. Toh and J. Su, ”NLANGP: Supervised Machine Learning System for Aspect Category Classification and Opinion TargetExtraction,” in Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2016), Denver,2015.
- A. Severyn and A. Moschitti, ”UNITN: training deep convolutional neural Network for twitter snetiment classification,” in Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), Denver,2015.
- P. Liu, S. Joty and H. Meng, ”Fine-grained opinion mining with recurrent neural networks and word embeddings,” in Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Lisbon,2015.A. N. Farhan and M. L. Khodra, ”Sentiment-specific word embedding for Indonesian sentiment analysis,” in ICAICTA,2017.
- Pennington, Jeffrey, Richard Socher, and Christopher D. Manning. ”Glove: Global vectors for word representation.” Proceedings of the Empiricial Methods in Natural Language Processing (EMNLP 2014) 12 (2014).
- Mikolov, Tomas, IlyaSutskever, Kai Chen, Greg S. Corrado, and Jeff Dean. ”Distributed representations of words and phrases and their com- positionality.” In Advances in Neural Information Processing Systems, pp. 3111-3119.2013.
- Svetlana Kiritchenko, Xiaodan Zhu, Colin Cherry, and Saif Mohammad, “Nrc-canada-2014: Detecting aspects and sentiment in customer reviews,” in Proceedings of the8th International Workshop on Semantic Evaluation, 2014, pp.437–442.
- Duy-Tin Vo and YueZhang, “Target-dependent twitter sentiment clas- sification with rich automatic features.,” in IJCAI, 2015, pp.1347–1353.
- Kai Sheng Tai, Richard Socher, and Christopher D Manning, “Improved semantic representations from tree structured long short-term memory networks,” arXiv preprint arXiv:1503.00075,2015.
- Meishan Zhang, YueZhang, and Duy-Tin Vo, “Gated neural networks for targeted sentiment analysis.,” inAAAI, 2016, pp. 3087–3093.
- Peng Chen, Zhongqian Sun, Lidong Bing, and Wei Yang, “Recurrent attention network on memory for aspect sentiment analysis,” in Proceed- ings of the 2017 Conference on Empirical Methods in Natural Language Processing, 2017, pp.452–46.
Index Terms
Keywords