PolyHope：Tweets中的两级希望语音检测
PolyHope: Two-Level Hope Speech Detection from Tweets
摘要：
希望的特征是对未来的精神开放，一种愿望，期望和希望某事发生或成为现实，显着影响人类的精神状态，情绪，行为和决定。希望通常与期望的期望和关于未来的可能性/概率的概念有关。尽管它的重要性，希望很少被研究作为一个社会媒体分析任务。本文提出了一个希望语音数据集，它首先将每条推文分为“希望”和“不希望”，然后分为三个细粒度的希望类别：“广义希望”，“现实希望”和“不现实的希望”（以及“不希望”）。收集2022年上半年的英文推文以建立此数据集。此外，我们详细描述了我们的注释过程和指导方针，并讨论了分类希望的挑战和现有的希望语音检测语料库的局限性。此外，我们还报告了几个基于不同学习方法的基线，如传统的机器学习、深度学习和Transformers，以基准测试我们的数据集。我们使用加权平均和宏观平均F1分数评估我们的基线。观察表明，严格的注释者选择过程和详细的注释指南提高了数据集的质量。这种严格的注释过程导致了只有二元语法的简单机器学习分类器的良好性能;然而，二进制和多类希望语音检测结果表明，上下文嵌入模型在该数据集中具有更高的性能。
英文摘要：
Hope is characterized as openness of spirit toward the future, a desire, expectation, and wish for something to happen or to be true that remarkably affects human's state of mind, emotions, behaviors, and decisions. Hope is usually associated with concepts of desired expectations and possibility/probability concerning the future. Despite its importance, hope has rarely been studied as a social media analysis task. This paper presents a hope speech dataset that classifies each tweet first into "Hope" and "Not Hope", then into three fine-grained hope categories: "Generalized Hope", "Realistic Hope", and "Unrealistic Hope" (along with "Not Hope"). English tweets in the first half of 2022 were collected to build this dataset. Furthermore, we describe our annotation process and guidelines in detail and discuss the challenges of classifying hope and the limitations of the existing hope speech detection corpora. In addition, we reported several baselines based on different learning approaches, such as traditional machine learning, deep learning, and transformers, to benchmark our dataset. We evaluated our baselines using weighted-averaged and macro-averaged F1-scores. Observations show that a strict process for annotator selection and detailed annotation guidelines enhanced the dataset's quality. This strict annotation process resulted in promising performance for simple machine learning classifiers with only bi-grams; however, binary and multiclass hope speech detection results reveal that contextual embedding models have higher performance in this dataset.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
