# PolyHope: Two-Level Hope Speech Detection from Tweets

arXiv: https://arxiv.org/abs/2210.14136 · HTML: https://mlsanity.com/paper/2210.14136/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 64 references that are in the map.
- First posted on arXiv 25 Oct 2022.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [Hope Speech Detection: A Computational Analysis of the Voice of Peace](https://mlsanity.com/paper/1909.12940/) (2019)
- [NLP-CUET@LT-EDI-EACL2021: Multilingual Code-Mixed Hope Speech Detection using Cross-lingual Representation Learner](https://mlsanity.com/paper/2103.00464/) (2021)

## Built on by (most cited first, 14 of 14)

- [Social Support Detection from Social Media Texts](https://mlsanity.com/paper/2411.02580/) (2024)
- [Optimism, Expectation, or Sarcasm? Multi-Class Hope Speech Detection in Spanish and English](https://mlsanity.com/paper/2504.17974/) (2025)
- [Guilt Detection in Text: A Step Towards Understanding Complex Emotions](https://mlsanity.com/paper/2303.03510/) (2023)
- [Advanced Machine Learning Techniques for Social Support Detection on Social Media](https://mlsanity.com/paper/2501.03370/) (2025)
- [Hope Speech Detection in code-mixed Roman Urdu tweets: A Positive Turn in Natural Language Processing](https://mlsanity.com/paper/2506.21583/) (2025)
- [GuReT: Distinguishing Guilt and Regret related Text](https://mlsanity.com/paper/2401.16541/) (2024)
- [Exploring Sentiment Dynamics and Predictive Behaviors in Cryptocurrency Discussions by Few-Shot Learning with Large Language Models](https://mlsanity.com/paper/2409.02836/) (2024)
- [EmoHopeSpeech: An Annotated Dataset of Emotions and Hope Speech in English and Arabic](https://mlsanity.com/paper/2505.11959/) (2025)
- [Multilingual Hope Speech Detection: A Comparative Study of Logistic Regression, mBERT, and XLM-RoBERTa with Active Learning](https://mlsanity.com/paper/2509.20315/) (2025)
- [Detecting Hope Across Languages: Multiclass Classification for Positive Online Discourse](https://mlsanity.com/paper/2509.25752/) (2025)
- [Hope Speech Detection in Social Media English Corpora: Performance of Traditional and Transformer Models](https://mlsanity.com/paper/2510.23585/) (2025)
- [Classification of Hope in Textual Data using Transformer-Based Models](https://mlsanity.com/paper/2511.12874/) (2025)
- [GHaLIB: A Multilingual Framework for Hope Speech Detection in Low-Resource Languages](https://mlsanity.com/paper/2512.22705/) (2025)
- [Towards Expectation Detection in Language: A Case Study on Treatment Expectations in Reddit](https://mlsanity.com/paper/2602.15504/) (2026)

## Abstract (arXiv)

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.