RESOLVING REVIEW RATING DISCREPANCIES IN TELECOMMUNICATIONS USING ARTIFICIAL INTELLIGENCE-BASED NEURAL SENTIMENT CLASSIFIERS AND LLM-BASED CHATBOTS
DOI:
https://doi.org/10.65164/5v1zsk74Kalit so‘zlar:
Artificial Intelligence, Sentiment Analysis, Neural Sentiment Classifiers, Large Language Models, Chatbots, Review Rating Inconsistency, Opinion Mining, Telecom Products, Customer Feedback Analysis, Natural Language Processing, Hybrid AI Framework, Text ClassificationAbstrak
Customer reviews play a key role in exploring products before purchasing.
Nevertheless, traditional star ratings between 1 and 5 do not always reflect the true sentiment
expressed in the accompanying text, especially in ambiguous cases where an assigned star is 2, 3 or
4. Although, a product itself might be high quality but non-product-related factors such as poor
customer service or late delivery leads to inconsistencies and poor ratings. The same applies to
positive reviews where a customer did not want to be overly negative and well-rated a product despite
it being low quality. This article presents a hybrid AI framework designed to resolve such ambiguities
in the IT and Telecom field and the related products – such as mobile devices, Wi-Fi routers and SIM
cards – by integrating Neural sentiment classifiers and Large Language Models (LLMs). Our model
helps conversational agents (Chatbots) to identify inconsistencies in review-ratings and infer the true
product sentiment with increased accuracy.
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