Aspect-Based Sentiment Analysis for Online Products
DOI:
https://doi.org/10.33423/jmdc.v18i4.7371Keywords:
Maketing development, T5 model, aspect-based sentiment analysis, aspect extractionAbstract
A number of resources are now available for gauging public opinion about businesses, products, services, brands, and events because of the advent of e-commerce. Customers may find it difficult to distinguish between legitimate assessments and biased or false reviews, which leads to worse goods or services. Aspect-based sentiment analysis is utilized in this study to develop a natural language processing model to examine customer reviews and comments to ascertain the sentiment expressed towards certain qualities or components of a good or service. A few procedures involved in this undertaking include aspect extraction, sentiment classification, training, feature extraction, and dataset preparation. The T5 model will be used to train the model using a dataset of customer reviews, and it will be evaluated using a variety of performance measures, including accuracy (84.62%), precision (84.58%), recall (84.62%), and F1-score (83.39%). The project's findings will help customers locate services that meet their needs, and they will also assist businesses in learning more about the advantages and disadvantages of their goods and services.
References
Abdulganiyu, O.H., Kabir, U., & Kakudi, H.A. (2021). Emoticon aware aspect based sentiment analysis of online product review. Dutse Journal of Pure and Applied Sciences (DUJOPAS), 7(2a). https://doi.org/10.1007/978-3-319-75487-1_19
Akhtar, M., Ekbal, A., & Bhattacharyya, P. (2018). Aspect based sentiment analysis: Category detection and sentiment classification for Hindi. https://doi.org/10.1007/978-3-319-75487-1_19
Barnaghi, P., Kontonatsios, G., Bessis, N., & Korkontzelos, I. (2018). Aspect extraction from reviews using convolutional neural networks and embeddings. DOI: 10.1007/978-3-030-23281-8_37.
Brychcín, T., Konkol, M., & Steinberger, J. (2014). UWB: Machine Learning Approach to Aspect-Based Sentiment Analysis. Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014). Doi: 10.3115/v1/S14-2145.
Fauzi, M. (2018). Word2Vec model for sentiment analysis of product reviews in Indonesian language. International Journal of Electrical and Computer Engineering (IJECE), 9, 525. DOI:10.11591/ijece.v9i1.pp525-530
Guha, S., Joshi, A., & Varma, V. (2018). SIEL: Aspect based sentiment analysis in reviews. DOI:10.18653/v1/S15-2129.
Hoang, M. (2019). Aspect-based sentiment analysis using BERT. Nordic Conference of Computational Linguistics.
Olsson, F., & Handmark, G. (2020). Sentiment analysis and aspect extraction for business intelligence.
Onalaja, S., Romero, E., & Yun, B. (2018). Aspect-based sentiment analysis of movie reviews. SMU Data Science Review, 5(3), Article 10.
Pang, G., Lu, K., Zhu, X., He, J., Mo, Z., Peng, Z., & Pu, B. (2019). Aspect-level sentiment analysis approach via BERT and aspect feature location model. Wireless Communications and Mobile Computing. DOI: 10.1155/2021/5534615.
Pimpa, C.P. (n.d.). Comparing SVM and naïve bayes classifiers for online reviews based on aspect-based sentiment analysis. Faculty of Business Administration Asia-Pacific International University.
Sivakumar, Reddy, U., & Srinivasulu, M. (2020). Aspect-based sentiment analysis of mobile phone reviews using LSTM and fuzzy logic. International Journal of Data Science and Analytics, 12. DOI: 10.1007/s41060-021-00277-x.
Sowmya, B., & John, P.T. (2018). Aspect Based Sentiment Analysis: Approaches and Algorithms. International Journal of Innovative Science and Research Technology (IJISRT), pp. 1668–1671. ISSN-2456-2165. https://doi.org/10.5281/zenodo.7223089
Srivastava, S., & Yiran, Y. (2019). Aspect-based Sentiment Analysis on mobile phone reviews with LDA. International Conference on Machine Learning Technologies, pp. 101–105. DOI:10.1145/3340997.3341012
Stanford, Y.Y. (2016). Aspect-based Sentiment Analysis on Hotel Reviews.
