A Deep Learning Approach to Sentiment Analysis of Customer Feedback for Enhanced Business Intelligence

Abstract

This paper proposes the use of deep learning to analyse the sentiment of customer feedback for business process intelligence. As customer-created data is growing exponentially, companies need to develop efficient ways of deriving meaningful insight out of the text-based Customer Feedback. Conventional approaches to sentiment analysis often fail to fully account for the nuances and complexities of language usage when the type of information is more extensive. To handle such a challenge, the research uses deep learning networks with sophisticated models, including Long Short-Term Memory (LSTM) neural networks and Transformer, to perfectly bucket the customer sentiment into groups of positive, negative, and neutral sentiments. The models are trained on a rich pool of customer reviews, which make them highly transferrable or have wide industry coverage. Other preprocessing methods that are discussed in the paper are tokenization, lemmatization, and vectorization to enhance the efficiency and understandability of the models. The study further examines the effect of hyperparameter tuning and transfer learning towards sentimental accuracy and generalisation. Outcomes show a big difference in sentiment prediction in terms of classical machine learning algorithms, including support vector machines (SVMs) and random forests. Based on the results, there is room to believe in the power of deep learning models to revolutionize the process of analyzing customer feedback and give a business timely insights that serve as the basis of making better decisions and creating personalized customer experiences leading to overall business intelligence strategies.