@inproceedings{chen-etal-2025-enhancing-investment,
title = "Enhancing Investment Opinion Ranking through Argument-Based Sentiment Analysis",
author = "Chen, Chung-Chi and
Huang, Hen-Hsen and
Chen, Hsin-Hsi and
Takamura, Hiroya and
Kobayashi, Ichiro and
Miyao, Yusuke",
editor = "Inui, Kentaro and
Sakti, Sakriani and
Wang, Haofen and
Wong, Derek F. and
Bhattacharyya, Pushpak and
Banerjee, Biplab and
Ekbal, Asif and
Chakraborty, Tanmoy and
Singh, Dhirendra Pratap",
booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
month = dec,
year = "2025",
address = "Mumbai, India",
publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
url = "https://aclanthology.org/2025.ijcnlp-long.71/",
pages = "1305--1315",
ISBN = "979-8-89176-298-5",
abstract = "In the era of rapid Internet and social media development, individuals readily share their investment opinions online. The overwhelming volume of such opinions makes comprehensive evaluation impractical, highlighting the need for an effective recommendation system that can identify valuable insights. To address this challenge, we propose an argument-based sentiment analysis framework that incorporates a new perspective on opinion strength. Our approach introduces the concept of a Fuzzy Strength Degree (FSD), derived from the difference between analysts' target and closing prices, to quantify the intensity of opinions. By integrating argument mining techniques, we further decompose each opinion into claims and premises, examine their relationships, and use these structures to evaluate the persuasive strength of the arguments. This dual strategy allows us to rank both professional and amateur investor opinions without relying on user history or social signals. Experiments show that our method works best for analyst reports, while on social media, simpler approaches based on wording and professionalism features perform better. Moreover, our analysis of professional analysts' and traders' behaviors reveals that top-ranked opinions are more likely to influence subsequent market actions. These findings demonstrate that argument structure and quantified opinion strength provide a novel and reliable foundation for investment opinion recommendation."
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<abstract>In the era of rapid Internet and social media development, individuals readily share their investment opinions online. The overwhelming volume of such opinions makes comprehensive evaluation impractical, highlighting the need for an effective recommendation system that can identify valuable insights. To address this challenge, we propose an argument-based sentiment analysis framework that incorporates a new perspective on opinion strength. Our approach introduces the concept of a Fuzzy Strength Degree (FSD), derived from the difference between analysts’ target and closing prices, to quantify the intensity of opinions. By integrating argument mining techniques, we further decompose each opinion into claims and premises, examine their relationships, and use these structures to evaluate the persuasive strength of the arguments. This dual strategy allows us to rank both professional and amateur investor opinions without relying on user history or social signals. Experiments show that our method works best for analyst reports, while on social media, simpler approaches based on wording and professionalism features perform better. Moreover, our analysis of professional analysts’ and traders’ behaviors reveals that top-ranked opinions are more likely to influence subsequent market actions. These findings demonstrate that argument structure and quantified opinion strength provide a novel and reliable foundation for investment opinion recommendation.</abstract>
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%0 Conference Proceedings
%T Enhancing Investment Opinion Ranking through Argument-Based Sentiment Analysis
%A Chen, Chung-Chi
%A Huang, Hen-Hsen
%A Chen, Hsin-Hsi
%A Takamura, Hiroya
%A Kobayashi, Ichiro
%A Miyao, Yusuke
%Y Inui, Kentaro
%Y Sakti, Sakriani
%Y Wang, Haofen
%Y Wong, Derek F.
%Y Bhattacharyya, Pushpak
%Y Banerjee, Biplab
%Y Ekbal, Asif
%Y Chakraborty, Tanmoy
%Y Singh, Dhirendra Pratap
%S Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics
%D 2025
%8 December
%I The Asian Federation of Natural Language Processing and The Association for Computational Linguistics
%C Mumbai, India
%@ 979-8-89176-298-5
%F chen-etal-2025-enhancing-investment
%X In the era of rapid Internet and social media development, individuals readily share their investment opinions online. The overwhelming volume of such opinions makes comprehensive evaluation impractical, highlighting the need for an effective recommendation system that can identify valuable insights. To address this challenge, we propose an argument-based sentiment analysis framework that incorporates a new perspective on opinion strength. Our approach introduces the concept of a Fuzzy Strength Degree (FSD), derived from the difference between analysts’ target and closing prices, to quantify the intensity of opinions. By integrating argument mining techniques, we further decompose each opinion into claims and premises, examine their relationships, and use these structures to evaluate the persuasive strength of the arguments. This dual strategy allows us to rank both professional and amateur investor opinions without relying on user history or social signals. Experiments show that our method works best for analyst reports, while on social media, simpler approaches based on wording and professionalism features perform better. Moreover, our analysis of professional analysts’ and traders’ behaviors reveals that top-ranked opinions are more likely to influence subsequent market actions. These findings demonstrate that argument structure and quantified opinion strength provide a novel and reliable foundation for investment opinion recommendation.
%U https://aclanthology.org/2025.ijcnlp-long.71/
%P 1305-1315
Markdown (Informal)
[Enhancing Investment Opinion Ranking through Argument-Based Sentiment Analysis](https://aclanthology.org/2025.ijcnlp-long.71/) (Chen et al., IJCNLP-AACL 2025)
ACL
- Chung-Chi Chen, Hen-Hsen Huang, Hsin-Hsi Chen, Hiroya Takamura, Ichiro Kobayashi, and Yusuke Miyao. 2025. Enhancing Investment Opinion Ranking through Argument-Based Sentiment Analysis. In Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics, pages 1305–1315, Mumbai, India. The Asian Federation of Natural Language Processing and The Association for Computational Linguistics.