Extracting Consumer Insight from Text: A Large Language Model Approach to Emotion and Evaluation Measurement

TL;DR

This paper introduces LX, a large language model fine-tuned for extracting consumer emotions and evaluations from text, outperforming existing models and enabling scalable, validated marketing insights from consumer-generated content.

Contribution

The study presents LX, a novel LLM trained on consumer data, achieving high accuracy in emotion and evaluation detection, and provides a no-code web tool for practical application.

Findings

LX outperforms GPT-4 Turbo, RoBERTa, and DeepSeek in accuracy.

Emotions expressed in reviews predict product ratings and purchase behavior.

Some emotions directly influence purchases beyond ratings.

Abstract

Accurately measuring consumer emotions and evaluations from unstructured text remains a core challenge for marketing research and practice. This study introduces the Linguistic eXtractor (LX), a fine-tuned, large language model trained on consumer-authored text that also has been labeled with consumers' self-reported ratings of 16 consumption-related emotions and four evaluation constructs: trust, commitment, recommendation, and sentiment. LX consistently outperforms leading models, including GPT-4 Turbo, RoBERTa, and DeepSeek, achieving 81% macro-F1 accuracy on open-ended survey responses and greater than 95% accuracy on third-party-annotated Amazon and Yelp reviews. An application of LX to online retail data, using seemingly unrelated regression, affirms that review-expressed emotions predict product ratings, which in turn predict purchase behavior. Most emotional effects are mediated by product ratings, though some emotions, such as discontent and peacefulness, influence purchase directly, indicating that emotional tone provides meaningful signals beyond star ratings. To support its use, a no-code, cost-free, LX web application is available, enabling scalable analyses of consumer-authored text. In establishing a new methodological foundation for consumer perception measurement, this research demonstrates new methods for leveraging large language models to advance marketing research and practice, thereby achieving validated detection of marketing constructs from consumer data.