Deciphering Negative Feedback through Handwritten Text

Abstract

In the contemporary realm of digital communication, this study delves into a novel approach to analyzing sentiments, with a specific focus on handwritten negative reviews. The research aims to improve the precision of sentiment analysis by exploring the inclusion of handwritten text, providing distinctive insights into consumer feedback, and refining business strategies. Taking a historical perspective, the investigation traces the evolution of sentiment analysis within the domain of natural language processing, leading to the central question: "How can the accuracy of sentiment analysis for negative reviews be enhanced through the incorporation of handwritten text?" This question guides an exploration of the challenges and potentials associated with merging handwriting analysis with conventional sentiment analysis methods. The study puts forth hypotheses that address the expected advantages of this integration, seeking to develop an innovative framework capable of not only accurately detecting negative sentiments but also considering the individuality inherent in handwritten expressions. The research methodology encompasses a review of existing literature and empirical analysis, resulting in the creation of a unique hybrid sentiment analysis algorithm that assesses both textual and handwriting features. This work contributes to the advancement of sentiment analysis and has implications for businesses aiming to better understand and respond to negative consumer sentiments.