  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.
