Differentiating Approach and Avoidance from Traditional Notions of Sentiment in Economic Contexts

TL;DR

This paper introduces new machine learning techniques to differentiate Approach and Avoidance sentiments from traditional positive/negative sentiment in economic texts, emphasizing their distinct roles in financial decision-making.

Contribution

It develops methods to distinguish Approach and Avoidance sentiments at a fundamental semantic level, enhancing understanding of emotional drivers in economic contexts.

Findings

Avoidance sentiment is a distinct evaluative and action-oriented emotion.

Refined Avoidance word-list improves macroeconomic model performance.

Approach and Avoidance sentiments are semantically separable from positive/negative sentiment.

Abstract

There is growing interest in the role of sentiment in economic decision-making. However, most research on the subject has focused on positive and negative valence. Conviction Narrative Theory (CNT) places Approach and Avoidance sentiment (that which drives action) at the heart of real-world decision-making, and argues that it better captures emotion in financial markets. This research, bringing together psychology and machine learning, introduces new techniques to differentiate Approach and Avoidance from positive and negative sentiment on a fundamental level of meaning. It does this by comparing word-lists, previously constructed to capture these concepts in text data, across a large range of semantic features. The results demonstrate that Avoidance in particular is well defined as a separate type of emotion, which is evaluative/cognitive and action-orientated in nature. Refining the Avoidance word-list according to these features improves macroeconomic models, suggesting that they capture the essence of Avoidance and that it plays a crucial role in driving real-world economic decision-making.