81
Views
2
CrossRef citations to date
0
Altmetric
Computers and Computing

CNN-OLSTM: Convolutional Neural Network with Optimized Long Short-Term Memory Model for Twitter based Sentiment Analysis

&

REFERENCES

  • M. Lovelin, P. Felciah, and R. Anbuselvi, “A study on sentiment analysis of social media reviews,” in 2015 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), Coimbatore, 2015, pp. 1–3.
  • P. Samiei, and A. K. Tripathi, “Effect of social networks on online reviews,” in 2014 47th Hawaii International Conference on System Sciences, Waikoloa, HI, 2014, pp. 1444–1453.
  • F. Zhu, and X. Zhang, “Impact of online consumer reviews on sales: the moderating role of product and consumer characteristics,” J. Mark., Vol. 74, no. 2, pp. 133–148, 2010.
  • S. M. Amrita, and R. Mohan, “Application of social media as a marketing promotion tool — a review,” in Computational Intelligence and Computing Research (ICCIC), 2016 IEEE International Conference, Chennai, India, Dec. 15–17, 2016.
  • Jamilah, and P. W. Handayani, “Analysis on effects of brand community on brand loyalty in the social media: a case study of an online transportation (UBER),” in International Conference on Advanced Computer Science and Information Systems (ICACSIS), 2016.
  • J. Acosta, N. Lamaute, M. Luo, E. Finkelstein, and A. Cotoranu. “Sentiment analysis of Twitter messages using Word2Vec.” (2017).
  • B. O. Deho, A. W. Agangiba, L. F. Aryeh, and A. J. Ansah, “Sentiment analysis with word embedding,” in 2018 IEEE 7th International Conference on Adaptive Science & Technology (ICAST), IEEE, 2018, pp. 1–4.
  • B. M. Jadav, and V. B. Vaghela, “Sentiment analysis using support vector machine based on feature selection and semantic analysis. Int. J. Comput. Appl., Vol. 146, no. 13, pp. 26–30, 2016.
  • Y. Al Amrani, M. Lazaar, and K. E. El Kadiri, “Random forest and support vector machine based hybrid approach to sentiment analysis,” Procedia. Comput. Sci., Vol. 127, pp. 511–520, 2018.
  • M. S. Mubarok, Adiwijaya, and M. D. Aldhi, “Aspect-based sentiment analysis to review products using Naïve Bayes,” AIP Conf. Proc., Vol. 1867, no. 1, p. 020060, 2017. AIP Publishing LLC.
  • A. Sharma, and S. Dey, “An artificial neural network based approach for sentiment analysis of opinionated text,” in Proceedings of the 2012 ACM Research in Applied Computation Symposium, 2012, pp. 37–42.
  • O. Daeli, and A. Faomasi, “Sentiment analysis on movie reviews using Information gain and K-nearest neighbor,” J. Data Sci. Appl., Vol. 3, no. 1, pp. 1–7, 2020.
  • L. Zhang, S. Wang, and B. Liu, “Deep learning for sentiment analysis: a survey,” WIRES Data Min. Knowl. Discov., Vol. 8, no. 4, p. e1253, 2018.
  • H. Pouransari, and S. Ghili. Deep learning for sentiment analysis of movie reviews. Technical report, Stanford University, 2014.
  • Q. T. Ain, M. Ali, A. Riaz, A. Noureen, M. Kamran, B. Hayat, and A. Rehman, “Sentiment analysis using deep learning techniques: a review,” Int J. Adv. Comput. Sci. Appl, Vol. 8, no. 6, p. 424, 2017.
  • K. Sentamilselvan, D. Aneri, A. C. Athithiya, and P. K. Kumar, “Twitter sentiment analysis using machine learning techniques,” Int. J. Eng. Adv. Technol., Vol. 9, no. 3, pp. 4205–4209, 2020.
  • A. Shah, K. Kothari, U. Thakkar, and S. Khara, “User review classification and star rating prediction by SA and machine learning classifiers,” in Information and Communication Technology for Sustainable Development, Singapore: Springer, 2020, pp. 279–288.
  • R. Srinivasan, and C. N. Subalalitha, “SA from imbalanced code-mixed data using machine learning approaches,” Distrib. Parallel Databases, 1–16, 2021.
  • M. Thomas, and C. A. Latha, “Sentimental analysis using recurrent neural network,” Int. J. Eng. Technol., Vol. 7, no. 2.27, pp. 88–92, 2018.
  • J. Shobana, and M. Murali, “An efficient sentiment analysis methodology based on long short-term memory networks,” Complex Intell. Syst., Vol. 7, no. 5, pp. 1–17, 2021.
  • U. D. Gandhi, P. M. Kumar, G. C. Babu, and G. Karthick, “Sentiment analysis on twitter data by using convolutional neural network (CNN) and long short term memory (LSTM),” Wirel. Pers. Commun., 1–10, 2021.
  • K. Sangeetha, and D. Prabha, “Sentiment analysis of student feedback using multi-head attention fusion model of word and context embedding for LSTM,” J. Ambient. Intell. Humaniz. Comput., Vol. 12, pp. 1–10, 2020.
  • S. Liao, J. Wang, R. Yu, K. Sato, and Z. Cheng, “CNN for situations understanding based on sentiment analysis of twitter data,” Procedia. Comput. Sci., Vol. 111, pp. 376–381, 2017.
  • E. Karthik, and T. Sethukarasi, “A centered convolutional restricted boltzmann machine optimized by hybrid atom search arithmetic optimization algorithm for sentimental analysis,” Neural Process. Lett., Vol. 54, no. 5, pp. 1–29, 2022.
  • A. Umair, and E. Masciari, “Sentimental and spatial analysis of COVID-19 vaccines tweets,” J. Intell. Inf. Syst., 1–21, 2022.
  • A. Onan, “Bidirectional convolutional recurrent neural network architecture with group-wise enhancement mechanism for text sentiment classification,” J. King Saud Univ. Comput. Inf. Sci., Vol. 34, no. 5, pp. 2098–2117, 2022.
  • A. Onan, “Mining opinions from instructor evaluation reviews: a deep learning approach,” Comput. Appl. Eng. Educ., Vol. 28, no. 1, pp. 117–138, 2020.
  • A. Onan, and M. A. Toçoglu, “A term weighted neural language model and stacked bidirectional LSTM based framework for sarcasm identification,” IEEE. Access., Vol. 9, pp. 7701–7722, 2021.
  • A. Onan, “An ensemble scheme based on language function analysis and feature engineering for text genre classification,” J. Inf. Sci., Vol. 44, no. 1, pp. 28–47, 2018.
  • A. Onan, “Biomedical text categorization based on ensemble pruning and optimized topic modelling,” Comput. Math. Methods. Med., Vol. 2018, pp. 1–22, 2018.
  • A. Onan, “Topic-enriched word embeddings for sarcasm identification,” in Computer science on-line conference, Cham: Springer, 2019, pp. 293–304.
  • A. U. Rehman, A. K. Malik, B. Raza, and W. Ali, “A hybrid CNN-LSTM model for improving accuracy of movie reviews sentiment analysis,” Multimed. Tools. Appl., Vol. 78, no. 18, pp. 26597–26613, 2019.
  • P. M. Sosa, Twitter sentiment analysis using combined LSTM-CNN models. Eprint Arxiv, 1–9, 2017.
  • M. Divyapushpalakshmi, and R. Ramalakshmi, “An efficient sentimental analysis using hybrid deep learning and optimization technique for Twitter using parts of speech (POS) tagging,” Int. J. Speech Technol., Vol. 24, no. 2, pp. 329–339, 2021.
  • S. S. Kumar, and A. Rajini, “Airline tweets sentimental analysis using Adaptive rider optimization based support vector neural network,” in 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT), IEEE, Jul. 2020, pp. 1–10.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.