The Geometry of Culture: Analyzing Meaning through Word Embeddings (1803.09288v1)

Abstract: We demonstrate the utility of a new methodological tool, neural-network word embedding models, for large-scale text analysis, revealing how these models produce richer insights into cultural associations and categories than possible with prior methods. Word embeddings represent semantic relations between words as geometric relationships between vectors in a high-dimensional space, operationalizing a relational model of meaning consistent with contemporary theories of identity and culture. We show that dimensions induced by word differences (e.g. man - woman, rich - poor, black - white, liberal - conservative) in these vector spaces closely correspond to dimensions of cultural meaning, and the projection of words onto these dimensions reflects widely shared cultural connotations when compared to surveyed responses and labeled historical data. We pilot a method for testing the stability of these associations, then demonstrate applications of word embeddings for macro-cultural investigation with a longitudinal analysis of the coevolution of gender and class associations in the United States over the 20th century and a comparative analysis of historic distinctions between markers of gender and class in the U.S. and Britain. We argue that the success of these high-dimensional models motivates a move towards "high-dimensional theorizing" of meanings, identities and cultural processes.

Analyzing Culture through Word Embeddings: Methodological Advancements and Applications

The paper "The Geometry of Culture: Analyzing Meaning through Word Embeddings" by Kozlowski et al. presents a compelling application of neural-network word embedding models for advanced text analysis, specifically targeting cultural associations and categorizations. The authors argue that the high-dimensional vector representations inherent in word embeddings yield richer semantic insights than traditional text analysis methods, promoting a data-driven approach for understanding identity and cultural processes.

The central premise of the paper is the alignment between dimensions induced by word embeddings and culturally significant dimensions such as gender, race, and class. The authors demonstrate this alignment by showcasing how words fall along semantic vectors such as "man – woman" or "rich – poor," finding consistent correlations with cultural perceptions when compared to survey data. This ability to capture complex cultural dimensions underscores the potential of word embeddings in sociological inquiry.

Methodological Insights

From a methodological perspective, the paper underscores the advantages of word embeddings over earlier models like Latent Semantic Analysis (LSA). Word embeddings position words based on shared contexts in a large corpus, allowing for the discernment of finer semantic relationships. By creating vector representations, these models allow for the assessment of both direct semantic relationships and the analogy-solving capabilities that reveal indirect associations.

The authors extend this methodology to investigate historical shifts in cultural associations within texts produced throughout the 20th century in the U.S. and compare cultural distinctions between the U.S. and Britain. These analyses demonstrate the robustness of word embeddings across different contexts, offering a comprehensive tool for longitudinal and cross-cultural studies in sociology.

Quantitative Validation

A significant strength of the paper lies in its quantitative validation. The authors correlate the projections from word embedding models with survey responses on gender, class, and race associations, showing strong correlations (e.g., 0.88 for gender using Google News embedding). Such validation highlights the capability of word embeddings to accurately reflect contemporary cultural sentiments.

The paper also explores how cultural associations shift over time and differ across regions. By calculating angles between vectors to evaluate evolving cultural dimensions, the authors reveal shifts in the intersectionality of concepts such as gender and class throughout the 20th century. This quantitative rigor provides a systematic approach to cultural analysis, reducing subjective biases that have challenged prior interpretive methods in social research.

Practical and Theoretical Implications

Practically, the research suggests that word embedding models can serve as valuable tools for researchers examining the socio-cultural narrative embedded within extensive text corpora. This is particularly poignant given the rapid digitization of textual data—it allows scholars to analyze cultural meaning and associations on a scale previously unattainable with manual coding or simpler machine learning methods.

Theoretically, the paper prompts a reevaluation of cultural models. The authors advocate for "high-dimensional theorizing," which accommodates the complexity and nuance of cultural systems better than lower-dimensional models. This perspective challenges traditional low-dimensional cultural theories, suggesting that identities and sociocultural dynamics are inherently multidimensional and interlinked.

Future Directions

While the paper emphasizes Euclidean geometries for capturing semantic dimensions, it raises the possibility of employing other geometric approaches like hyperbolic embeddings for capturing hierarchical or non-linear relationships within culture. Future research may explore modifying hidden parameters such as curvature to enhance the model's capabilities across different semantic tasks.

In conclusion, the paper presents neural network-driven word embeddings as powerful instruments for cultural analysis, pushing the boundaries of what can be discerned about cultural systems from natural language data. By providing a rigorous methodological framework backed by strong empirical validation, it paves the way for further exploration and exploitation of these models in sociological research and beyond.