Applications of Machine Learning-based Sentiment Analysis in Marketing and Brand Monitoring

Abstract

In the online marketing arena, large volumes of textual information, generated by clients, on social media sites, and online review sites and brand communities are informative in terms of consumer perception and brand attitudes. The unstructured and massive nature of this data however renders the traditional analysis methods inefficient. This paper explores the use of machine learning-driven sentiment analysis in marketing and brand monitoring by examining customer feedback of various sources on the internet. Pre-processing of the data was done through the common natural language processing methods and the data was categorized using a number of machine learning models, such as, the Logistic Regression, Support Vector Machines, Decision Tree, and the Random Forest. Accuracy, precision, recall, and F1-score quantitative assessment prove that ensemble-based models perform better in sentiment classification. The analysis also shows how an insight of sentiment can be utilized to determine a marketing campaign success, brand reputation, and comparative brand analysis. The results affirm that sentiment analysis based on machine learning can allow data-driven marketing, increase customer interest, and proactive brand management.