APPLICATION OF NATURAL LANGUAGE PROCESSING TO AUTOMATE THE ANALYSIS OF USER FEEDBACK
DOI:
https://doi.org/10.60022/sis.3.(02).5Keywords:
natural language processing, analysis of responses, aspect-oriented analysis, machine learningAbstract
The purpose of the research is to develop a system for automated analysis of user feedback based on natural language processing methods to identify sentiments, key aspects and themes. The relevance of the research topic is due to the growing need for businesses in effective tools for analyzing large volumes of text data, which would allow extracting valuable insights from user feedback and transforming them into specific recommendations for improving products and services. This topic is of particular importance in the context of e-commerce, services and software development, where the quality and speed of response to user feedback directly affect the competitiveness of companies. Research methods include machine learning, deep neural networks, statistical text analysis methods and methods for assessing the quality of models. Classical algorithms, neural network models, transformer architectures are used. The statistical significance of the results obtained was experimentally confirmed and recommendations for selecting models for various scenarios were developed. The relevance of the research topic is due to the growing business need for effective tools for analyzing large volumes of text data, which would allow extracting valuable insights from user reviews and transforming them into specific recommendations for improving products and services. The research results can be used in e-commerce, service companies, software development, marketing, and analytics to automate review analysis and identify trends in large volumes of text data.
References
Acheampong, F. A., Wenyu C., Nunoo-Mensah H. Text-based emotion detection: Advances, challenges, and opportu- nities. Engineering Reports. 2020. Vol. 2, No. 7.
Amazon Comprehend Documentation [Електронний реcурc] // Amazon Web Services. 2024. URL: https://docs.aws.amazon.com/comprehend/.
Amazon Reviews Dataset [Електронний реcурc] // Registry of Open Data on AWS. 2023. URL: https://registry. opendata.aws/amazon-reviews/.
Bird, S., Klein, E., Loper, E. Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit. O’Reilly Media, 2019. 504 p.
Chen, Z., Qian, T. Transfer Capsule Network for Aspect Level Sentiment Classification. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2019. P. 547–556.
Conneau, A. Unsupervised Cross-lingual Representation Learning at Scale. Proc. ACL 2020, pp. 440–451.
Devlin, J., Chang, M.W., Lee, K., Toutanova K. BERT: Pre-training of Deep Bidirectional Transformers for Lan- guage Understanding. Proceedings of NAACL-HLT 2019. 2019. P. 171–186.
Global AI Survey: AI proves its worth, but few scale impact [Електронний реcурc] // McKinsey & Company. 2023. URL: https://www.mckinsey.com/capabilities/quantumblack/our-insights/global-ai-survey-ai-proves-its-worth-but-few-scale-impact.
Goldberg, Y. Neural Network Methods for Natural Language Processing. Synthesis Lectures on Human Language Technologies. 2017. Vol. 10, No. 1. P. 1–309.
Google Cloud Natural Language API [Електронний реcурc] // Google Cloud. 2024. URL: https://cloud.google.com/natural-language.
Hugging, Face Transformers Documentation [Електронний реcурc] // Hugging Face. 2024. URL: https://hugging-face.co/docs/transformers/.
Jurafsky, D., Martin, J.H. Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition. 3rd ed. draft. Pearson, 2023. [Електронний реcурc]. URL: https://web.stanford.edu/~jurafsky/slp3/.
Kim, Y. Convolutional Neural Networks for Sentence Classification. Proceedings of the 2014 Conference on Empir- ical Methods in Natural Language Processing (EMNLP). 2014. P. 746–751.
Liu, Y., Ott M., Goyal N. et al. RoBERTa: A Robustly Optimized BERT Pretraining Approach [Електронний реcурc] // arXiv preprint. 2019. URL: https://arxiv.org/abs/1907.11692.
Mikolov, T., Chen K., Corrado G., Dean J. Efficient Estimation of Word Representations in Vector Space. Proceed- ings of the International Conference on Learning Representations (ICLR 2013). 2013. P. 16–19.
NLTK Documentation [Електронний реcурc] // Natural Language Toolkit. 2024. URL: https://www.nltk.org/.
NLP Progress: Sentiment Analysis [Електронний реcурc] // NLP Progress. 2023. URL: http://nlpprogress.com/english/sentiment_analysis.html.
Pontiki, M., Galanis D., Papageorgiou H. et al. SemEval-2016 Task 5: Aspect Based Sentiment Analysis. Proceed- ings of the 10th International Workshop on Semantic Evaluation. 2016. P. 19–30.
Poria S., Cambria E., Gelbukh A. Aspect Extraction for Opinion Mining with a Deep Convolutional Neural Net- work. Knowledge-Based Systems. 2016. Vol. 108. P. 42–49.
PyTorch Documentation [Електронний реcурc] // PyTorch. 2024. URL: https://pytorch.org/docs/stable/.
Sanh, V., Debut L., Chaumond J., Wolf T. DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter [Електронний реcурc] // arXiv preprint. 2019. URL: https://arxiv.org/abs/1910.01108.
SemEval-2022 Tasks [Електронний реcурc] // International Workshop on Semantic Evaluation. 2022. URL: https://semeval.github.io/SemEval2022/tasks.html.
Yelp Open Dataset [Електронний реcурc] // Yelp. 2023. URL: https://www.yelp.com/dataset.
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