Enhancing Investment Opinion Ranking through Argument-Based Sentiment Analysis

TL;DR

This paper presents an argument-based sentiment analysis method to improve investment opinion ranking, aiming to enhance recommendation systems by identifying high-profit potential opinions and analyzing associated risks.

Contribution

It introduces a dual argument mining approach that considers price discrepancies and opinion scoring, advancing investment opinion ranking techniques.

Findings

Effective identification of high-profit opinions

Improved ranking accuracy through argument mining

Insights into risk and investor behavior relationships

Abstract

In the era of rapid Internet and social media platform development, individuals readily share their viewpoints online. The overwhelming quantity of these posts renders comprehensive analysis impractical. This necessitates an efficient recommendation system to filter and present significant, relevant opinions. Our research introduces a dual-pronged argument mining technique to improve recommendation system effectiveness, considering both professional and amateur investor perspectives. Our first strategy involves using the discrepancy between target and closing prices as an opinion indicator. The second strategy applies argument mining principles to score investors' opinions, subsequently ranking them by these scores. Experimental results confirm the effectiveness of our approach, demonstrating its ability to identify opinions with higher profit potential. Beyond profitability, our research extends to risk analysis, examining the relationship between recommended opinions and investor behaviors. This offers a holistic view of potential outcomes following the adoption of these recommended opinions.