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Machine Learning and Sentiment Analysis for Analyzing Customer Feedback: A Review
Abstract
The rapid transformation in the business domain enhances the understanding that to achieve competitive advantage, corporates need to understand customer sentiments. The abundance of customer data as customer feedback, product reviews, and posts on social media platforms provides an in-depth insight that can navigate strategic decisions and inflate customer experiences. In this context, the unification of machine learning and sentiment analysis emerges as a potent combination for extracting emotional traces from volumes of unstructured text data. This chapter searches into the sphere of analysis techniques of sentiment analysis for analyzing customer feedback, where the convergence of advanced machine learning techniques with sentiment analysis methods empowers businesses to derive valuable insights from the feedback gathered from various touch points. By decoding sentiments and opinions hidden within textual data, this approach enables organizations to capture a clear view on customer satisfaction, identify their pain points, uncover emerging trends, and tailor offerings accordingly.
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