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Articles

Unleashing the power of 2D CNN with attention and pre-trained embeddings for enhanced online review analysis

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Pages 46-57 | Received 21 Jun 2023, Accepted 08 Nov 2023, Published online: 06 Dec 2023

References

  • Yu KH, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nature Biomedical Engineering. 2018;2(10):719–731. doi:10.1038/s41551-018-0305-z
  • Cao L. Ai in finance: A review. Available at SSRN. 2020;3647625.
  • Di Vaio A, Palladino R, Hassan R, et al. Artificial intelligence and business models in the sustainable development goals perspective: A systematic literature review. J Bus Res. 2020;121:283–314. doi:10.1016/j.jbusres.2020.08.019
  • Fu D, Houlette R. Putting AI in entertainment: an AI authoring tool for simulation and games. IEEE Intell Syst. 2002;17(4):81–84. doi:10.1109/MIS.2002.1024756
  • Mohammed Elsaid Moussa, E.H.M., Haggag, MH. A generic lexicon-based framework for sentiment analysis. Int J Comput Appl 42(5), 463–473 (2020). doi:10.1080/1206212X.2018.1483813
  • Alaei AR, Becken S, Stantic B. Sentiment analysis in tourism: capitalizing on big data. Journal of Travel Research. 2019;58(2):175–191. doi:10.1177/0047287517747753
  • Nanath K, Joy G. Leveraging twitter data to analyze the virality of COVID-19 tweets: a text mining approach. Behav Inf Technol. 2023;42(2):196–214. doi:10.1080/0144929X.2021.1941259
  • Uma Maheswari S, Dhenakaran S. Opinion mining on integrated social networks and e-commerce blog. IETE J Res. 2023;69(4):2080–2088. doi:10.1080/03772063.2021.1886603
  • Xu G, Meng Y, Qiu X, et al. Sentiment analysis of comment texts based on bilstm. Ieee Access. 2019;7:51522–51532. doi:10.1109/ACCESS.2019.2909919
  • ¸ alı, S., Balaman, SY. Improved decisions for marketing, supply and purchasing: mining big data through an integration of sentiment analysis and intuitionistic fuzzy multi criteria assessment. Comput Ind Eng 129, 315– 332 2019. doi:10.1016/j.cie.2019.01.051
  • Kokab ST, Asghar S, Naz S. Transformer-based deep learning models for the sentiment analysis of social media data. Array. 2022;14:100157. doi:10.1016/j.array.2022.100157
  • Ullah MA, Marium SM, Begum SA, et al. An algorithm and method for sentiment analysis using the text and emoticon. ICT Express. 2020;6(4):357–360. doi:10.1016/j.icte.2020.07.003
  • Kalaivani K, Uma S, Kanimozhiselvi C. A review on feature extraction techniques for sentiment classification. 2020 fourth international conference on computing methodologies and communication (ICCMC); 2020: pp. 679–683. IEEE.
  • Sharma Y, Agrawal G, Jain P, et al. Vector representation of words for sentiment analysis using glove. 2017 international conference on intelligent communication and computational techniques (icct); 2017: pp. 279–284. IEEE.
  • Al-Saqqa S, Awajan A. The use of word2vec model in sentiment analysis: A survey. Proceedings of the 2019 International Conference on Artificial Intelligence, Robotics and Control; 2019: 39–43.
  • Santos I, Nedjah N, de Macedo Mourelle L. Sentiment analysis using convo-lutional neural network with fasttext embeddings. 2017 IEEE Latin American conference on computational intelligence (LA-CCI); 2017: pp. 1–5. IEEE.
  • Selva Birunda S, Kanniga Devi R. A review on word embedding techniques for text classification. Innovative Data Communication Technologies and Application: Proceedings of ICIDCA 2020 pp. 267–281; 2021.
  • Sohangir S, Wang D, Pomeranets A, et al. Big data: deep learning for financial sentiment analysis. J Big Data. 2018;5:1–25. doi:10.1186/s40537-017-0111-6
  • Barry, J.: Sentiment analysis of online reviews using bag-of-words and lstm approaches. AICS. pp. 272–274 (2017)
  • Sachin S, Tripathi A, Mahajan N, et al. Sentiment analysis using gated recurrent neural networks. SN Computer Science. 2020;1:1–13. doi:10.1007/s42979-020-0076-y
  • Liu J, Zheng S, Xu G, et al. Cross-domain sentiment aware word embeddings for review sentiment analysis. International Journal of Machine Learning and Cybernetics. 2021;12:343–354. doi:10.1007/s13042-020-01175-7
  • Hamid NIA, Kamal NAM, Hanum HFM, et al. Fprosentiment analysis on mobile phone brands reviews using convolutional neural network (CNN). In: 2022 IEEE International Conference on Computing (ICOCO); 2022: 102–107. doi:10.1109/ICOCO56118.2022.10031660
  • Katić T, Milićević N. Comparing sentiment analysis and document representation methods of Amazon reviews. 2018 IEEE 16th International SympoSium on Intelligent Systems and Informatics (SISY); 2018: pp. 000000283–000286. doi:10.1109/SISY.2018.8524814
  • Rezaeinia SM, Rahmani R, Ghodsi A, et al. Sentiment analysis based on improved pre-trained word embeddings. Expert Syst Appl. 2019;117:139–147. doi:10.1016/j.eswa.2018.08.044
  • Qorich M, El Ouazzani R. Text sentiment classification of Amazon reviews using. word embeddings and convolutional neural networks. The Journal of Supercom-Puting. 2023;79:11029–11054. https://doi.org/10.1007/s11227-023-05094-6
  • Suhartono, D., Purwandari, K., Jeremy, N.H., Philip, S., Arisaputra, P., Parmo-nangan, I.H. Deep neural networks and weighted word embeddings for sentiment analysis of drug product reviews. Procedia Comput Sci. 2023;216:664–671. doi:10.1016/j.procs.2022.12.182
  • Santosh Kumar P, Yadav RB, Dhavale SV. A comparison of pre-trained word embeddings for sentiment analysis using deep learning. In: international conference on innovative computing and communications: proceedings of ICICC 2020; 2021, pp. 525–537. Springer.
  • Pennington J, Socher R, Manning C. GloVe: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in NatUral Language Processing (EMNLP). pp. 1532–1543. Association for Computational Linguistics, Doha, Qatar (Oct 2014). https://aclanthology.org/D14-1162.
  • Khomsah S, Ramadhani R, Wijaya S. The accuracy comparison between Word2Vec and FastText On sentiment analysis of hotel reviews. Jur-nal RESTI (Rekayasa Sistem dan Teknologi Informasi). 2022;6:352–358. doi:10.29207/resti.v6i3.3711
  • Al-Saqqa S, Awajan A. The use of word2vec model in sentiment analysis: A survey; 2019. 10.1145/3388218.3388229.
  • Rong X. Word2vec parameter learning explained. arXiv preprint arXiv:1411.2738.
  • Sun, X., Lu, W.: Understanding attention for text classification; 2020: pp. 3418–3428. doi:10.18653/v1/2020.acl-main.312
  • Nibras, G.: Amazon cell phones reviews. https://www.kaggle.com/dsv/861634; 2019.
  • Nagadia M. Amazon kindle book review for sentiment analysis.https://www.kaggle.com/datasets/meetnagadia/amazon-kindle-book-reviewfor-sentiment-analysis (2023).
  • Mitra S, Jenamani, M. Hybrid improved document-level embedding (hide); 2020. arXiv preprint arXiv:2006.01203

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