Decoding Consumer Sentiments: Advanced NLP Techniques for Analyzing Smartphone Reviews
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Shaista Jabeen
Abstract
Objectives: this study aims to bridge the gap in effectively analyzing online consumer feedback on smartphones, which is often voluminous and linguistically complex. The ultimate goal is to provide smartphone manufacturers with actionable insights to refine product features and marketing strategies. We propose a dual-model framework using bidirectional encoder representations from transformers (BERT) and
sentence transformers for sentiment analysis and topic modeling, respectively. This approach is intended to enhance the accuracy and depth of consumer sentiment analysis. Method: sentiment analysis and topic modeling are applied to a large dataset of smartphone reviews sourced from Kaggle and Amazon. The BERT model is used to understand the context and sentiment of words, while sentence transformers generate embeddings for clustering reviews into thematic topics. Results: our analysis revealed strong positive sentiments regarding smartphone performance and user experience, while also identifying concerns about camera and battery life. However, while the model effectively captures positive feedback, it may struggle with negative feedback and especially neutral sentiments, due to the dataset’s bias toward positive reviews. Conclusions: the application of BERT and sentence transformers provides a significant technological advancement in the field of text analysis by enhancing the granularity of sentiment detection and offering a robust framework for interpreting complex data sets. This contributes to both theoretical knowledge and practical applications in digital consumer analytics
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Jabeen, S. (2024). Decoding Consumer Sentiments: Advanced NLP Techniques for Analyzing Smartphone Reviews. Journal of Contemporary Administration, 28(4), e240102. https://doi.org/10.1590/1982-7849rac2024240102.en
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Hu, M., & Liu, B. (2004). Mining and summarizing customer reviews. In Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 168-177.
Huang, J., Sun, H., Guo, P., Zhao, M., & Niu, K. (2016). Fine-grained sentimental tendency analysis based on Chinese online commentary of mobile phone. In Proceedings of the 2016 IEEE International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), 319-323.
Jelodar, H., Wang, Y., Yuan, C., Feng, X., Jiang, X., Li, Y., & Zhao, L. (2019). Latent Dirichlet allocation (LDA) and topic modeling: Models, applications, a survey. Multimedia tools and applications, 78, 15169-15211. https://doi.org/10.1007/s11042-018-6894-4
Jeong, B., Yoon, J., & Lee, J.-M. (2019). Social media mining for product planning: A product opportunity mining approach based on topic modeling and sentiment analysis. International Journal of Information Management, 48, 280-290. https://doi.org/10.1016/j.ijinfomgt.2017.09.009
Joung, J., & Kim, H. M. (2021). Automated keyword filtering in latent Dirichlet allocation for identifying product attributes from online reviews. Journal of Mechanical Design, 143(8), 084501. https://doi.org/10.1115/1.4048960
Kaur, G., & Sharma, A. (2023). A deep learning-based model using hybrid feature extraction approach for consumer sentiment analysis. Journal of Big Data, 10(1), 5. https://doi.org/10.1186/s40537-022-00680-6
Kotler, P., & Keller, K. L. (2016). Marketing management (15th ed.). Pearson Education.
Kumar, G., & Parimala, N. (2020). An integration of sentiment analysis and MCDM approach for smartphone recommendation. International Journal of Information Technology & Decision Making, 19(4), 1037-1063. https://doi.org/10.1142/S021962202050025X
Latif, S., Qadir, J., Farooq, S., & Imran, M. A. (2017). How 5G wireless (and concomitant technologies) will revolutionize healthcare?. Future Internet, 9(4), 93. https://doi.org/10.3390/fi9040093
Li, H., & Ma, L. (2020). Charting the path to purchase using topic models. Journal of Marketing Research, 57(6), 1019-1036. https://doi.org/10.1177/0022243720954376
Li, L. (2011). Introduction: Advances in e-business engineering. Information Technology and Management, 12(2), 49-50. https://doi.org/10.1007/s10799-011-0100-y
Li, P., Huang, L., & Ren, G. J. (2020). Topic detection and summarization of user reviews. arXiv preprint. https://doi.org/10.48550/arXiv.2006.00148
Liu, Z. (2024). A financial analysis of Apple based on its external and internal environment. Journal of Education, Humanities and Social Sciences, 30, 97-104. https://doi.org/10.54097/tyj6y326
Mahadevan, A., & Arock, M. (2020). Integrated topic modeling and sentiment analysis: A review rating prediction approach for recommender systems. Turkish Journal of Electrical Engineering and Computer Sciences, 28(1), 107-123. https://doi.org/10.3906/elk-1905-114
Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams engineering journal, 5(4), 1093-1113. https://doi.org/10.1016/j.asej.2014.04.011
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