Sentiment Analysis with Hidden Markov Models for Enhanced Customer Insights

In the era of digital connectivity, businesses increasingly rely on customer feedback and online reviews as valuable sources of information for improving their products and services. The accurate analysis of customer sentiments is pivotal in this endeavor, but it comes with its set of challenges, including the intricacies of human language, such as sarcasm and ambiguity. To address these challenges, this research introduces a solution by harnessing the power of Hidden Markov Models (HMMs) for automated sentiment analysis. HMMs provide a probabilistic framework capable of systematically categorizing customer reviews into positive, negative, or neutral sentiments. The study's workflow encompasses several crucial steps. It starts with the meticulous collection and preprocessing of data to ensure a diverse and clean dataset. Subsequently, the data is labeled, enabling more efficient sentiment analysis, and feature extraction is carried out using the Bag-of-Words technique. HMMs take the center stage in this research, leveraging their capability to model sentiment transitions and observation symbols effectively. The experimental results underscore the effectiveness of the proposed system, as it accurately classifies customer sentiments. Moreover, the Viterbi algorithm, employed for sentiment prediction, efficiently gauges the sentiment orientation of previously unseen reviews. Results showcase the system's effectiveness, achieving 92.5% accuracy across review counts. Precision reaches 94.0%, recall at 92.1%, and F1 scores at 94.3%. The Viterbi algorithm efficiently predicts sentiment orientation for unseen reviews. Mean Absolute Error (MAE) values indicate precise sentiment score predictions (0.069 to 0.124). HMM outperforms in Mean Squared Error (MSE) at 0.005 and Root Mean Squared Error (RMSE) at 0.151 for 1000 reviews. In essence, this research serves as a testament to the robustness of HMMs, showcasing their ability to extract invaluable insights from customer feedback. This, in turn, empowers businesses to make data-driven decisions, thereby enhancing their products and services to better align with evolving customer needs and expectations.