| Challenge: | Sentiment analysis studies focus more on online customer reviews and social media texts, but are less on literary studies. |
| Approach: | They propose to model the perceived sentiment of Iliad verses using a deep learning masked language model and a pre-trained model to estimate the sentiment of the poem. |
| Outcome: | The proposed model shows that sentiment estimators can be used as mechanical annotators, thus facilitating the distant reading of Homeric text. |
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Deciphering Emotional Landscapes in the Iliad: A Novel French-Annotated Dataset for Emotion Recognition (2024.lrec-main)
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| Challenge: | Using an emotion-annotated dataset, we aim to provide a resource for the scientific community to study the emotional intricacies of classical literature. |
| Approach: | They propose to provide an emotion-annotated dataset for classical literature and Western mythology using a multivariate time series and a deep learning masked language model. |
| Outcome: | The proposed dataset reveals compelling patterns and phenomena within the Iliad's emotional landscape. |
MultiBooked: A Corpus of Basque and Catalan Hotel Reviews Annotated for Aspect-level Sentiment Classification (L18-1)
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| Challenge: | sentiment analysis research has focused on unsupervised or semi-supervised approaches, but these still require a large number of resources and do not reach the performance of supervised approaches. |
| Approach: | They propose two datasets for supervised aspect-level sentiment analysis in Basque and Catalan. |
| Outcome: | The proposed datasets are based on two under-resourced languages, basque and catalan. |
Annotated Corpus for Sentiment Analysis in Odia Language (2020.lrec-1)
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| Challenge: | Existing sentiment analysis models are not available for Odia 1 as it is a resource-poor language. |
| Approach: | They create an annotated Odia corpus and test its usability by training and testing on the corpus using various classifiers. |
| Outcome: | The created corpus contains 2045 Odia sentences from news domain annotated with sentiment labels using a well-defined annotation scheme. |
Role-Guided Annotation and Prototype-Aligned Representation Learning for Historical Literature Sentiment Classification (2025.findings-emnlp)
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| Challenge: | Prior work focused on using sentiment lexicons or leveraging large language models for annotation . lexiconics are often unavailable for historical texts due to limited linguistic resources . |
| Approach: | They propose a role-guided annotation strategy that prompts LLMs to simulate historical perspectives when labeling sentiment. |
| Outcome: | The proposed method outperforms state-of-the-art baselines across historical literature datasets. |
A Comprehensive Survey of Contemporary Arabic Sentiment Analysis: Methods, Challenges, and Future Directions (2025.findings-naacl)
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| Challenge: | Existing literature on Arabic sentiment analysis is limited, compared to high-resourced languages such as English and French. |
| Approach: | They present a systematic review of existing literature on Arabic sentiment analysis focusing on research utilizing deep learning. |
| Outcome: | The proposed methods highlight gaps in the literature on Arabic sentiment analysis and outline promising directions for future research. |
Development and Evaluation of Pre-trained Language Models for Historical Danish and Norwegian Literary Texts (2024.lrec-main)
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| Challenge: | et al., 2019) develop and evaluate the first pre-trained language models specifically tailored for historical Danish and Norwegian texts. |
| Approach: | They develop and evaluate pre-trained language models specifically tailored for historical Danish and Norwegian texts. |
| Outcome: | The proposed model outperforms models trained on historical Danish and Norwegian literature in two downstream NLP tasks. |
Exploring Amharic Sentiment Analysis from Social Media Texts: Building Annotation Tools and Classification Models (2020.coling-main)
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| Challenge: | Existing crowdsourcing platforms do not support sentiment analysis for Amharic, and there are no expert researchers in the area. |
| Approach: | They propose to build a social-network-friendly Amharic sentiment analysis tool using the Telegram bot and collect 9.4k tweets where each tweet is annotated by three Telegram users. |
| Outcome: | The proposed system outperforms existing classifiers in Amharic and other low-resource languages due to the widespread use of sarcasm and figurative speech. |
Sentiment Analysis in the Era of Large Language Models: A Reality Check (2024.findings-naacl)
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| Challenge: | Sentiment analysis (SA) has been a long-standing research area in natural language processing. |
| Approach: | They propose a benchmark to evaluate LLMs' SA abilities and propose 'sentiEval' benchmark to be used for a more comprehensive evaluation. |
| Outcome: | The proposed benchmark outperforms small language models on 26 datasets on 13 tasks and compared them with LLMs trained on domain-specific datasets. |
Odi et Amo. Creating, Evaluating and Extending Sentiment Lexicons for Latin. (2020.lrec-1)
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| Challenge: | a new paper aims to provide sentiment analysis tools for ancient languages . the current sentiment analysis resources only cover modern languages based on textual typologies . |
| Approach: | They propose to use manually-curated Latin lexicons to evaluate sentiment analysis tools . they propose a gold standard and a silver standard for evaluating lexical items . |
| Outcome: | The proposed lexicons are evaluated using a gold standard and a silver standard for sentiment analysis. |
Encoding Sentiment Information into Word Vectors for Sentiment Analysis (C18-1)
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| Challenge: | Existing methods for embedding sentiment knowledge into word vectors are generally trained independently of the downstream task. |
| Approach: | They propose to encode sentiment knowledge into pre-trained word vectors to improve sentiment analysis. |
| Outcome: | The proposed method improves sentiment analysis on four popular sentiment datasets compared to benchmark methods. |