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.

本文介绍了一个用于分类 Twitter 推文的希望言论数据集，并提供了基于不同学习方法的多项基准测试以进行评估和分析。研究中发现，使用严格的注释过程并提供详细的注释指南可以提高数据集的质量。同时，使用上下文嵌入模型进行希望言论检测有更高的性能表现。