Abstract
In this paper we explore the challenges of measuring sentiment in relation to Environmental, Social and Governance (ESG) social media. ESG has grown in importance in recent years with a surge in interest from the financial sector and the performance of many businesses has become based in part on their ESG related reputations. The use of sentiment analysis to measure ESG related reputation has developed and with it interest in the use of machines to do so. The era of digital media has created an explosion of new media sources, driven by the growth of social media platforms. This growing data environment has become an excellent source for behavioural insight studies across many disciplines that includes politics, healthcare and market research. Our study seeks to compare human performance with the cutting edge in machine performance in the measurement of ESG related sentiment. To this end researchers classify the sentiment of 150 tweets and a reliability measure is made. A gold standard data set is then established based on the consensus of 3 researchers and this data set is then used to measure the performance of different machine approaches: one based on the VADER dictionary approach to sentiment classification and then multiple language model approaches, including Llama2, T5, Mistral, Mixtral, FINBERT, GPT3.5 and GPT4.
Abstract (translated)
在本文中，我们探讨了在环境、社会和治理（ESG）社交媒体上衡量情感的挑战。近年来，ESG的重要性随着金融部门兴趣的增加和许多企业的业绩部分基于其ESG相关声誉而增加。使用情感分析来衡量ESG相关声誉的发展，以及机器在这方面应用的兴趣不断增加。数字媒体时代的爆炸性增长带来了大量新的媒体来源，主要由社交媒体平台的增长推动。这个不断增长的数据环境已经成为许多学科领域行为洞察力研究的重要来源，包括政治、医疗和市场研究。我们的研究旨在比较人类表现与机器在测量ESG相关情感方面的尖端表现。为此，研究人员将150条推文的情绪进行了分类，并进行了可靠性度量。然后，基于三名研究人员的共识，建立了一个黄金标准数据集。接着，将这个数据集用于衡量不同机器方法的性能：基于VADER词典方法的情绪分类和多种语言模型方法，包括Llama2、T5、Mistral、Mixtral、FINBERT、GPT3.5和GPT4。
URL
https://arxiv.org/abs/2402.16650
