NLP-BASED INTER AND INTRA-SENTENCE RELATIONSHIP  ANALYSIS-AWARE BANK CUSTOMER BEHAVIOR ANALYSIS AND  PREFERENCE DETECTION USING GLSNSTM

Rajesh Kumar Kanji, Vinodkumar Reddy Surasani Naveen Kumar Kotha and Uday Kiran Chilakalapalli

NLP-BASED INTER AND INTRA-SENTENCE RELATIONSHIP ANALYSIS-AWARE BANK CUSTOMER BEHAVIOR ANALYSIS AND PREFERENCE DETECTION USING GLSNSTM

Authors

Rajesh Kumar Kanji, Vinodkumar Reddy Surasani Naveen Kumar Kotha and Uday Kiran Chilakalapalli

Keywords:

Natural Language Processing (NLP), Client data analysis, Part of Speech (PoS) tagging, Dependency Parsing (DP), Rhetorical Structure Theory (RST), Hidden Kneser–Ney Smoothing Markov Model (HKNSMM), Inter and Intra-sentence relationship analysis, and Syntactic structure analysis.

Abstract

Email and Chat logs are crucial information for examining customer transactionsand communication patterns to understand their preferences. The existing studies didn’tanalyze inter and intra-sentence relationship analysis between the chat logs/email for accurate
bank customer preference detection. Therefore, this paper presents an NLP-enabled bank customer behavior analysis

References

Abbasimehr, H., & Shabani, M. (2019). A new methodology for customer behavior analysis using time series clustering: A case study on a bank’s customers. Kybernetes, 50(2), 221–242. https://doi.org/10.1108/K-09-2018-0506

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Published

2023-11-20

How to Cite

Rajesh Kumar Kanji, Vinodkumar Reddy Surasani Naveen Kumar Kotha and Uday Kiran Chilakalapalli. (2023). NLP-BASED INTER AND INTRA-SENTENCE RELATIONSHIP ANALYSIS-AWARE BANK CUSTOMER BEHAVIOR ANALYSIS AND PREFERENCE DETECTION USING GLSNSTM. Journal of Computational Analysis and Applications (JoCAAA), 31(4), 1834–1857. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/3720