The interplay between mood and eating episodes has been extensively researched, revealing a connection between the two. Previous studies have relied on questionnaires and mobile phone self-reports to investigate the relationship between mood and eating. However, current literature exhibits several limitations: a lack of investigation into the generalization of mood inference models trained with data from various everyday life situations to specific contexts like eating; an absence of studies using sensor data to explore the intersection of mood and eating; and inadequate examination of model personalization techniques within limited label settings, a common challenge in mood inference (i.e., far fewer negative mood reports compared to positive or neutral reports). In this study, we sought to examine everyday eating behavior and mood using two datasets of college students in Mexico (N_mex = 84, 1843 mood-while-eating reports) and eight countries (N_mul = 678, 24K mood-while-eating reports), which contain both passive smartphone sensing and self-report data. Our results indicate that generic mood inference models experience a decline in performance in specific contexts, such as during eating, highlighting the issue of sub-context shifts in mobile sensing. Moreover, we discovered that population-level (non-personalized) and hybrid (partially personalized) modeling techniques fall short in the commonly used three-class mood inference task (positive, neutral, negative). To overcome these limitations, we implemented a novel community-based personalization approach. Our findings demonstrate that mood-while-eating can be inferred with accuracies 63.8% (with F1-score of 62.5) for the MEX dataset and 88.3% (with F1-score of 85.7) with the MUL dataset using community-based models, surpassing those achieved with traditional methods.