Age, Gender, and Sentiments: Navigating the Landscape of Review Interpretation

Abstract

Sentiment analysis serves as a powerful lens through which businesses gain invaluable insights into customer feedback, enabling them to enhance products and services, thereby boosting sales. The intricate web of sentiments expressed by diverse customers necessitates a nuanced approach. Within this landscape, age and gender emerge as pivotal demographic factors influencing the accuracy of sentiment analysis in text reviews. The interplay between age and sentiment is fascinating. Younger individuals often weave informal language into their reviews, punctuating their expressions with extremes of emotion. In contrast, their older counterparts tend towards formality and a more measured emotional spectrum. Moreover, the gender dimension adds another layer to this intricate tapestry, with women and men showcasing unique ways of expressing emotions and distinct focal points in their textual reviews. Recognizing the impact of these demographic factors on sentiment analysis, our study takes a comprehensive approach. Leveraging Natural Language Processing (NLP) features, we delve into reviews encompassing a broad spectrum of age and gender demographics. By doing so, we aim to refine sentiment analysis models, ensuring a nuanced understanding of customer sentiments. In our proposed study, we meticulously analyze user reviews, constructing models that unravel the intricate dance between age, gender, and sentimental values. Employing a repertoire of machine learning algorithms, we scrutinize three distinct sets of attributes: all features, age features, and gender features. The results unveiled a compelling narrative, affirming the profound influence of age and gender on the nuanced landscape of sentimental values.

Our journey into sentiment analysis, entwined with machine learning prowess and enriched by NLP insights, yields a deeper understanding of customer sentiments.