Dating platforms have become a cornerstone of modern relationships, boasting millions of users worldwide. Each platform advertises itself using carefully crafted messaging to attract users. However, user experiences often tell a different story, highlighting frustrations with features, user behavior, or unmet expectations. By analyzing these divergent narratives, this project seeks to uncover the disparity between how platforms market themselves and how they are perceived.
To investigate the disconnect between advertising language and user perceptions, I conducted a text analysis of the two most popular dating platforms, Tinder and Hinge. The analysis utilized marketing campaigns (text from recent ads published by Tinder and Hinge) and user reviews (excerpts from articles that discuss personal experiences on these dating platforms. I applied natural language processing (NLP) techniques to analyze word frequency and sentiment. This approach enabled a comparative assessment of how each platform's branding align or diverges from user feedback.
The presentation pdf contains the final visualizations for this comparative text analysis using NLP.