Transforming Customer Experience through NLP and Sentiment Analysis

Paras Doshi1, Phani Chilakapati2, Ridhi Deora3 and Anant Agarwal4, 1Opendoor, USA, 2Industrial Scientific, USA, 3Home Depot, USA, 4Petco, USA

ABSTRACT

The integration of Sentiment Analysis (SA) and Natural Language Processing (NLP) assists companies in improving customer service by examining actionable insights from unstructured data sources like social media tweets and customer reviews. RoBERTa acts as a sophisticated transformer-based model that is capable of analyzing complex customer sentiment data. This paper assesses the performance of RoBERTa for sentiment classification while investigating solutions to class imbalance issues, sarcasm detection difficulties, and ethical issues. The discussion proposes optimization techniques for RoBERTa on different data sets to register high accuracy and outline future research directions to improve fairness and explainability. The paper concludes its material by introducing mathematical frameworks as performance metric tools in addition to optimization and evaluation procedures.

KEYWORDS

Natural Language Processing, Sentiment Analysis, RoBERTa, Transformer Models, Customer Experience, Text Classification, Data Imbalance, Predictive Analytics, Sarcasm Detection, Ethical AI

Original Source URL: https://aircconline.com/ijdkp/V15N3/15325ijdkp02.pdf

https://airccse.org/journal/ijdkp/vol15.html