LDPP at the FinNLP-2022 ERAI Task: Determinantal Point Processes and Variational Auto-encoders for Identifying High-Quality Opinions from a pool of Social Media Posts

Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP) Pub Date : 1900-01-01 DOI:10.18653/v1/2022.finnlp-1.18

Paul Trust, R. Minghim

{"title":"LDPP at the FinNLP-2022 ERAI Task: Determinantal Point Processes and Variational Auto-encoders for Identifying High-Quality Opinions from a pool of Social Media Posts","authors":"Paul Trust, R. Minghim","doi":"10.18653/v1/2022.finnlp-1.18","DOIUrl":null,"url":null,"abstract":"Social media and online forums have made it easier for people to share their views and opinions on various topics in society. In this paper, we focus on posts discussing investment related topics. When it comes to investment , people can now easily share their opinions about online traded items and also provide rationales to support their arguments on social media. However, there are millions of posts to read with potential of having some posts from amateur investors or completely unrelated posts. Identifying the most important posts that could lead to higher maximal potential profit (MPP) and lower maximal loss for investment is not a trivial task. In this paper, propose to use determinantal point processes and variational autoencoders to identify high quality posts from the given rationales. Experimental results suggest that our method mines quality posts compared to random selection and also latent variable modeling improves improves the quality of selected posts.","PeriodicalId":331851,"journal":{"name":"Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18653/v1/2022.finnlp-1.18","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 0

Abstract

Social media and online forums have made it easier for people to share their views and opinions on various topics in society. In this paper, we focus on posts discussing investment related topics. When it comes to investment , people can now easily share their opinions about online traded items and also provide rationales to support their arguments on social media. However, there are millions of posts to read with potential of having some posts from amateur investors or completely unrelated posts. Identifying the most important posts that could lead to higher maximal potential profit (MPP) and lower maximal loss for investment is not a trivial task. In this paper, propose to use determinantal point processes and variational autoencoders to identify high quality posts from the given rationales. Experimental results suggest that our method mines quality posts compared to random selection and also latent variable modeling improves improves the quality of selected posts.

查看原文本刊更多论文

在FinNLP-2022 ERAI任务中的LDPP:从社交媒体帖子池中识别高质量意见的决定性点过程和变分自编码器

社交媒体和在线论坛使人们更容易就社会上的各种话题分享自己的观点和意见。在本文中，我们主要关注讨论投资相关话题的帖子。在投资方面，人们现在可以很容易地分享他们对在线交易项目的看法，并在社交媒体上提供支持他们观点的理由。然而，有数以百万计的帖子可供阅读，其中可能有一些来自业余投资者或完全不相关的帖子。确定最重要的岗位，可能导致更高的最大潜在利润(MPP)和更低的最大投资损失不是一个简单的任务。在本文中，建议使用确定性点过程和变分自编码器从给定的原理中识别高质量的帖子。实验结果表明，与随机选择相比，我们的方法挖掘了优质帖子，并且潜在变量建模改进了所选帖子的质量。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP)

自引率

0.00%

发文量