Health experts assert that hope plays a crucial role in enhancing
individuals' physical and mental well-being, facilitating their recovery, and
promoting restoration. Hope speech refers to comments, posts and other social
media messages that offer support, reassurance, suggestions, inspiration, and
insight. The detection of hope speech involves the analysis of such textual
content, with the aim of identifying messages that invoke positive emotions in
people. Our study aims to find computationally efficient yet
comparable/superior methods for hope speech detection. We also make our
codebase public at this https URL

研究旨在寻找计算效率更高、可比较 / 优于现有方法的希望言论检测方法，并公开我们的代码库。

超越负面性：关于希望言论检测的重新分析和后续实验

Beyond Negativity: Re-Analysis and Follow-Up Experiments on Hope Speech  Detection

In recent years, several systems have been developed to regulate the spread
of negativity and eliminate aggressive, offensive or abusive contents from the
online platforms. Nevertheless, a limited number of researches carried out to
identify positive, encouraging and supportive contents. In this work, our goal
is to identify whether a social media post/comment contains hope speech or not.
We propose three distinct models to identify hope speech in English, Tamil and
Malayalam language to serve this purpose. To attain this goal, we employed
various machine learning (support vector machine, logistic regression,
ensemble), deep learning (convolutional neural network + long short term
memory) and transformer (m-BERT, Indic-BERT, XLNet, XLM-Roberta) based methods.
Results indicate that XLM-Roberta outdoes all other techniques by gaining a
weighted $f_1$-score of $0.93$, $0.60$ and $0.85$ respectively for English,
Tamil and Malayalam language. Our team has achieved $1^{st}$, $2^{nd}$ and
$1^{st}$ rank in these three tasks respectively.

本研究旨在利用机器学习、深度学习和转换器等自然语言处理技术，分别分析英语、泰米尔语和马拉雅拉姆语中社交媒体帖子或评论包含希望言论的可能性，并发现 XLM-Roberta 技术能够达到最佳的分析效果。

使用跨语言表示学习器进行多语言混合编码希望言论检测

NLP-CUET@LT-EDI-EACL2021: Multilingual Code-Mixed Hope Speech Detection  using Cross-lingual Representation Learner

This paper aims to describe the approach we used to detect hope speech in the
HopeEDI dataset. We experimented with two approaches. In the first approach, we
used contextual embeddings to train classifiers using logistic regression,
random forest, SVM, and LSTM based models.The second approach involved using a
majority voting ensemble of 11 models which were obtained by fine-tuning
pre-trained transformer models (BERT, ALBERT, RoBERTa, IndicBERT) after adding
an output layer. We found that the second approach was superior for English,
Tamil and Malayalam. Our solution got a weighted F1 score of 0.93, 0.75 and
0.49 for English,Malayalam and Tamil respectively. Our solution ranked first in
English, eighth in Malayalam and eleventh in Tamil.

本文介绍了使用上下文嵌入和预训练 transformer 模型的方法来检测 HopeEDI 数据集中的 Hope speech。实验表明，使用预训练 transformer 模型的方法在英语、泰米尔语和马拉雅拉姆语上均优于使用逻辑回归、随机森林、SVM 和 LSTM 的方法，F1 得分最高值达到了 0.93。