GADFA: Generator-Assisted Decision-Focused Approach for Opinion Expressing Timing Identification

TL;DR

This paper introduces a novel task of identifying the optimal timing for opinion expression triggered by news, using a decision-focused approach with text generation models to improve classification accuracy.

Contribution

It proposes a new task and dataset for opinion timing identification and develops a decision-focused method that leverages text generation to enhance classification performance.

Findings

Generated text provides new insights aiding timing identification

The approach improves classification accuracy

Demonstrates effectiveness on a novel dataset

Abstract

The advancement of text generation models has granted us the capability to produce coherent and convincing text on demand. Yet, in real-life circumstances, individuals do not continuously generate text or voice their opinions. For instance, consumers pen product reviews after weighing the merits and demerits of a product, and professional analysts issue reports following significant news releases. In essence, opinion expression is typically prompted by particular reasons or signals. Despite long-standing developments in opinion mining, the appropriate timing for expressing an opinion remains largely unexplored. To address this deficit, our study introduces an innovative task - the identification of news-triggered opinion expressing timing. We ground this task in the actions of professional stock analysts and develop a novel dataset for investigation. Our approach is decision-focused, leveraging text generation models to steer the classification model, thus enhancing overall performance. Our experimental findings demonstrate that the text generated by our model contributes fresh insights from various angles, effectively aiding in identifying the optimal timing for opinion expression.