A Sentiment Analysis Based Approach for Customer Segmentation

Background: Customer Segmentation is the process of dividing customers into groups based on some demographic factors in order to get an idea of the targeted audience for a product and to best market said product. Objective: Sentiment Analysis on customer reviews is one way that this process can be enhanced to get not just demographic information but subjective information and preferences as well. Methods: In this study, Long Short-Term Memory model, a deep learning technique, has been applied for Sentiment Analysis and its results have been used to perform Customer Segmentation on demographic data containing information such as age and gender. Segmentation was performed using Spectral Clustering. Cluster Labels were extracted to perform supervised classification using different supervised algorithms, such as Support Vector Machines, Random Forests, Decision Trees and Logistic Regression. Results: An accuracy of 90.9% was achieved by the LSTM model. An accuracy of 100% was achieved by the Random Forest and Decision Tree Classifiers.