Minghui Kuang, Ramin Safa, Seyyed Ahmad Edalatpanah, Robert S. Keyser

DOI Number
First page
Last page


Product reviews play a crucial role in providing valuable insights to consumers and producers. Analyzing the vast amount of data generated around a product, such as posts, comments, and views, can be challenging for business intelligence purposes. Sentiment analysis of this content helps both consumers and producers gain a better understanding of the market status, enabling them to make informed decisions. In this study, we propose a novel hybrid approach based on deep neural networks (DNNs) for sentiment analysis in product reviews, focusing on the classification of sentiments expressed. Our approach utilizes the recursive neural network (RNN) algorithm for sentiment classification. To address the imbalanced distribution of positive and negative samples in social network data, we employ a resampling technique that balances the dataset by increasing samples from the minority class and decreasing samples from the majority class. We evaluate our approach using Amazon data, comprising four product categories: clothing, cars, luxury goods, and household appliances. Experimental results demonstrate that our proposed approach performs well in sentiment analysis for product reviews, particularly in the context of digital marketing. Furthermore, the attention-based RNN algorithm outperforms the baseline RNN by approximately 5%. Notably, the study reveals consumer sentiment variations across different products, particularly in relation to appearance and price aspects.


Deep learning, Recursive neural network (RNN), Resampling technique, Social media marketing

Full Text:



Rout, J.K., Choo, K.K.R., Dash, A.K., Bakshi, S., Jena, S.K., Williams, K.L., 2018, A model for sentiment and emotion analysis of unstructured social media text, Electronic Commerce Research, 18, pp. 181-199.

Liu, H., Chatterjee, I., Zhou, M., Lu, X.S., Abusorrah, A., 2020, Aspect-based sentiment analysis: A survey of deep learning methods, IEEE Transactions on Computational Social Systems, 7(6), pp. 1358-1375.

Trueman, T.E., Cambria, E., 2021, A convolutional stacked bidirectional LSTM with a multiplicative attention mechanism for aspect category and sentiment detection, Cognitive Computation, 13, pp. 1423-1432.

Sadr, H., Pedram, M.M., Teshnehlab, M., 2019, A robust sentiment analysis method based on sequential combination of convolutional and recursive neural networks, Neural Processing Letters, 50, pp. 2745-2761.

Silva, N.F.F.D., Coletta, L.F., Hruschka, E.R., 2016, A survey and comparative study of tweet sentiment analysis via semi-supervised learning, ACM Computing Surveys (CSUR), 49(1), 15.

Dang, N.C., Moreno-García, M.N., De la Prieta, F., 2020, Sentiment analysis based on deep learning: A comparative study, Electronics, 9(3), 483.

Rong, W., Peng, B., Ouyang, Y., Li, C., Xiong, Z., 2013, Semi-supervised dual recurrent neural network for sentiment analysis, In 2013 IEEE 11th International Conference on Dependable, Autonomic and Secure Computing (pp. 438-445), Chengdu, China, IEEE.

Evans, D., Bratton, S., McKee, J., 2021, Social media marketing, AG Printing & Publishing.

Capatina, A., Kachour, M., Lichy, J., Micu, A., Micu, A.E., Codignola, F., 2020, Matching the future capabilities of an artificial intelligence-based software for social media marketing with potential users’ expectations, Technological Forecasting and Social Change, 151, 119794.

Mattila, M., Salman, H., 2018, Analysing social media marketing on twitter using sentiment analysis, KTH.

Kitsios, F., Kamariotou, M., Karanikolas, P., Grigoroudis, E., 2021, Digital marketing platforms and customer satisfaction: Identifying eWOM using big data and text mining, Applied Sciences, 11(17), 8032.

Chen, L.C., Lee, C.M., Chen, M.Y., 2020, Exploration of social media for sentiment analysis using deep learning, Soft Computing, 24, pp. 8187-8197.

Umer, M., Imtiaz, Z., Ahmad, M., Nappi, M., Medaglia, C., Choi, G. S., Mehmood, A., 2023, Impact of convolutional neural network and FastText embedding on text classification, Multimedia Tools and Applications, 82(4), 5569-5585.

Wang, G., Sun, J., Ma, J., Xu, K., Gu, J., 2014, Sentiment classification: the contribution of ensemble learning, Decision Support Systems, 57, pp. 77-93.

Ahmad, K. ed., 2011, Affective computing and sentiment analysis: emotion, metaphor and terminology, Springer Science & Business Media.

Liu, B., 2022, Sentiment analysis and opinion mining, Springer Nature.

Saumya, S., Singh, J.P., 2018, Detection of spam reviews: a sentiment analysis approach, CSI Transactions on ICT, 6(2), pp. 137-148.

Prusa, J., Khoshgoftaar, T.M., Dittman, D.J., Napolitano, A., 2015, Using random undersampling to alleviate class imbalance on tweet sentiment data, In 2015 IEEE International Conference on Information Reuse And Integration, pp. 197-202, San Francisco, CA, USA, IEEE.

Li, S., Wang, Z., Zhou, G., Lee, S.Y.M., 2011, Semi-supervised learning for imbalanced sentiment classification, In Twenty-Second International Joint Conference on Artificial Intelligence, Barcelona Catalonia Spain, AAAI Press.

Manning, C.D., Surdeanu, M., Bauer, J., Finkel, J.R., Bethard, S., McClosky, D., 2014, The stanford CoreNLP natural language processing toolkit, In Proceedings of 52nd Annual Meeting of the Association For Computational Linguistics: System Demonstrations, pp. 55-60, Baltimore, Maryland, Association for Computational Linguistics.

Hamilton, W.L., Clark, K., Leskovec, J., Jurafsky, D., 2016, Inducing domain-specific sentiment lexicons from unlabeled corpora, In Proceedings of the Conference on Empirical Methods In Natural Language Processing (Vol. 2016, p. 595), Austin, Texas, Association for Computational Linguistics.

Larson, K., Watson, R., 2011, The value of social media: toward measuring social media strategies, In ICIS 2011 Proceedings, Shanghai.

Foltean, F.S., Trif, S.M., Tuleu, D.L., 2019, Customer relationship management capabilities and social media technology use: Consequences on firm performance, Journal of Business Research, 104, pp. 563-575.

Sajid, S.I., 2016, Social media and its role in marketing, Business and Economics Journal, 7(1), 203.

Kaplan, A.M., Haenlein, M., 2012, Social media: back to the roots and back to the future, Journal of Systems and Information Technology, 14(2), pp. 101-104.

Bisio, F., Oneto, L., Cambria, E., 2017, Sentic computing for social network analysis, In Sentiment Analysis in Social Networks, Morgan Kaufmann, pp. 71-99.

Jamil, K., Dunnan, L., Gul, R.F., Shehzad, M.U., Gillani, S.H.M., Awan, F.H., 2022, Role of social media marketing activities in influencing customer intentions: a perspective of a new emerging era, Organizational Psychology, 12, 808525.

Ibrahim, B., 2022, Social media marketing activities and brand loyalty: A meta-analysis examination, Journal of Promotion Management, 28(1), pp. 60-90.

DOI: https://doi.org/10.22190/FUME230901038K


  • There are currently no refbacks.

ISSN: 0354-2025 (Print)

ISSN: 2335-0164 (Online)

COBISS.SR-ID 98732551

ZDB-ID: 2766459-4