Revolutionizing Customer Experience Through NLP and Sentiment Analysis Strategies

The objective of this paper is to highlight Bidirectional Long Short-Term Memory (BiLSTM) neural network’s role in improving Customer Experience (CX). Sentiment Analysis (SA) and Natural Language Processing (NLP) are used as primary methodology for interpreting real-time customer feedback and preferences. BiLSTM models can comprehend contextual understanding at a high level because it analyzes text bidirectionally. They are good models for reviews, social media post, or chat log analyses. This research work analyzes the effectiveness of BiLSTM for sentiment-driven decision-making, in real-time analysis of feedback, and customer engagement based on their personalized characteristics. As shown in the results section, although the model excelled at positivity detection, limitations with neutrality and negativity detection existed, attributed to imbalances within the dataset. Some integration concerns and scalability challenges along with some ethical issues concerning the algorithms like mitigating bias have been analyzed. The research offers a systematic framework to deploy BiLSTM towards customer-centric innovation.