Each person develops a unique emotional landscape shaped by their experiences and linguistic-cultural contexts, partly personal and partly shared with others. This enables personally unique emotional experiences while maintaining shared understanding. This work aims to advance a framework for investigating what's shared and distinct across individuals, beginning with linguistic communities as an essential level of analysis, using English and Korean speakers as our case study. We examined how emotion concept representations differ between English and Korean speakers using representational similarity analysis and network analysis. English and Korean speakers’ judgments of pairwise similarity between 57 emotion concepts evidenced both substantial shared structure and language-specific patterns (Spearman's ρ = 0.72, indicating 48% unshared variance). While valence emerged as a key organizing dimension in both languages, network analyses with strength centrality showed distinct patterns for each language. First, the Korean emotion concept network demonstrated higher strength centrality across all emotion concepts than the English network, indicating higher interconnectedness between concepts. Second, high-centrality emotions were predominantly negative in both languages but formed language-specific local networks with different sets of neighboring concepts. The statistics of language usage encode a substantial part of the conceptual structure of emotion, enabling large language models to capture aspects of human emotion. Despite their advanced multilingual capabilities, GPT4-o and Claude-3.5 showed stronger alignment with English speakers' representations, regardless of prompt language. These findings demonstrate that while languages reflect common principles in emotion representation, they shape distinct patterns, with implications for cross-linguistic/cultural emotion understanding and AI system development.