Improving Sentiment Analysis for Latin Poetry through Data Augmentation

Core Concepts

This paper presents two methods for automatically annotating Latin data to augment the limited training data available for emotion polarity detection, a task that is particularly challenging for rhetorical genres like poetry. The authors employ a variety of Latin language models in a neural architecture and achieve the second highest macro-averaged Macro-F1 score on the EvaLatin 2024 shared task test set.

Abstract

The paper addresses the task of emotion polarity detection in Latin, which is challenging due to the low-resource environment and the complexity of sentiment in rhetorical genres like poetry. To address the lack of training data, the authors propose two methods for automatic data annotation:

Polarity Coordinate (PC) Clustering: This method maps sentences onto a two-dimensional plane representing polarity and intensity, and then uses k-means clustering to classify the sentences into positive, negative, neutral, and mixed categories.

Gaussian Clustering: This method trains a Gaussian Mixture Model on the existing Odes dataset and uses the resulting class representations to classify new sentences.

The authors then employ a neural architecture with various Latin language models as embeddings and different encoder types (identity, BiLSTM, Transformer) to classify the automatically annotated data. They perform a hyperparameter search and submit the best-performing models to the EvaLatin 2024 shared task.
The results show that the Gaussian-annotated dataset generally outperforms the PC-annotated dataset, likely due to its more balanced distribution of classes. The authors' best submission achieved the second highest macro-averaged Macro-F1 score on the shared task test set, narrowly missing the top score. The authors analyze the performance of their models, noting that the Gaussian model's distribution bias towards the positive class may have contributed to its strong performance on the Pontano subset.
Overall, the paper demonstrates the potential of data augmentation techniques to improve sentiment analysis in low-resource settings, particularly for complex genres like Latin poetry.

Stats

The Odes dataset contains 44 labeled sentences with the following class distribution:
Positive: 20
Negative: 12
Neutral: 3
Mixed: 9
The PC dataset contains 75,505 examples with the following class distribution:
Positive: 10,427
Negative: 4,114
Neutral: 57,786
Mixed: 4,178
The Gaussian dataset contains 76,505 examples with the following class distribution:
Positive: 33,473
Negative: 14,333
Neutral: 16,861
Mixed: 11,838

Quotes

"Emotion polarity detection is a variant on the common NLP task of sentiment analysis. Usual applications of this task tend to be on reviews—for example, about movies (Maas et al., 2011; Socher et al., 2013) or products (Blitzer et al., 2007)—where providing an opinion is the author's goal. Few works have extended this task to less direct modalities of sentiment, like poetry, and even fewer to ancient languages, like Latin (Chen and Skiena, 2014; Marley, 2018; Sprugnoli et al., 2020, 2023)."
"To classify sentences, we used LatinAffectus-v4 as the crux of our scoring function. Each xi ∈x was searched in the lexicon. To search the lexicon, we used lemmata from the treebank sentences if they were available and the LatinBackoffLemmatizer from the Classical Language Toolkit (CLTK) as a backoff option (Johnson et al., 2021)."

Key Insights Distilled From

Nostra Domina at EvaLatin 2024

by Stephen Both... at arxiv.org 04-12-2024

https://arxiv.org/pdf/2404.07792.pdf

Deeper Inquiries

How could the automatic annotation methods be further refined to better capture the nuances of sentiment in Latin poetry?

In order to enhance the automatic annotation methods for sentiment analysis in Latin poetry, several refinements can be considered:

Fine-tuning Lexicon Entries: The sentiment lexicon used for scoring could be expanded and refined to include more nuanced sentiment indicators specific to Latin poetry. This could involve incorporating literary devices, cultural references, and stylistic elements that are prevalent in classical poetry.

Contextual Analysis: Implementing a more sophisticated contextual analysis approach that takes into account the broader context of the poem or the work as a whole. Understanding the thematic elements, narrative structure, and historical background could provide a more comprehensive view of sentiment.

Poetic Form Recognition: Developing algorithms that can recognize and analyze the specific poetic forms used in Latin poetry, such as meter, rhyme scheme, and stanza structure. These elements can influence the emotional impact of the text and should be considered in sentiment analysis.

Semantic Role Labeling: Incorporating semantic role labeling techniques to identify the relationships between entities, actions, and sentiments expressed in the text. This can help in capturing the subtle nuances of sentiment conveyed through complex linguistic structures.

Human-in-the-Loop Validation: Implementing a human-in-the-loop validation process where experts in Latin poetry review and provide feedback on the annotated data. This iterative approach can help refine the annotation methods based on domain-specific insights.

How might the insights from this work on Latin sentiment analysis be applied to other ancient or less-studied languages with limited available data?

The insights gained from the work on Latin sentiment analysis can be extrapolated and applied to other ancient or less-studied languages in the following ways:

Transfer Learning: Leveraging transfer learning techniques to adapt the sentiment analysis models trained on Latin data to other languages with limited resources. By fine-tuning the pre-trained models on the target language, it is possible to capture language-specific sentiment patterns.

Cross-Lingual Sentiment Analysis: Exploring cross-lingual sentiment analysis approaches that can transfer knowledge from well-resourced languages to low-resource languages. By identifying sentiment similarities and differences across languages, it is possible to improve sentiment analysis in less-studied languages.

Multimodal Analysis: Integrating multimodal data sources, such as images, audio, or historical context, to enrich the sentiment analysis process in ancient languages. This holistic approach can provide additional cues for understanding sentiment in texts where linguistic data is scarce.

Collaborative Research Initiatives: Engaging in collaborative research initiatives with scholars and experts in other ancient or less-studied languages to share methodologies, tools, and resources for sentiment analysis. By fostering interdisciplinary collaborations, it is possible to advance sentiment analysis in diverse linguistic contexts.

Resource Sharing: Establishing repositories and platforms for sharing annotated data, sentiment lexicons, and models specific to ancient languages. This open-access approach can facilitate knowledge exchange and accelerate research in sentiment analysis for under-resourced languages.

What other techniques, beyond data augmentation, could be explored to improve sentiment analysis for low-resource languages and genres?

In addition to data augmentation, several techniques can be explored to enhance sentiment analysis for low-resource languages and genres:

Semi-Supervised Learning: Implementing semi-supervised learning algorithms that can leverage a small amount of labeled data along with a larger pool of unlabeled data. This approach can effectively utilize limited resources for training sentiment analysis models.

Domain Adaptation: Employing domain adaptation techniques to transfer knowledge from related domains or languages to the target low-resource language or genre. By adapting pre-existing sentiment analysis models, it is possible to improve performance in specific contexts.

Active Learning: Incorporating active learning strategies to intelligently select the most informative data points for annotation. By iteratively labeling data instances that contribute the most to model improvement, active learning can optimize the use of limited labeling resources.

Ensemble Methods: Utilizing ensemble methods to combine predictions from multiple sentiment analysis models trained on different subsets of data or using diverse algorithms. Ensemble learning can enhance the robustness and generalization of sentiment analysis models in low-resource settings.

Zero-Shot Learning: Exploring zero-shot learning approaches that can infer sentiment labels for languages or genres without labeled training data. By leveraging transfer learning and multilingual embeddings, zero-shot learning enables sentiment analysis in novel contexts with minimal supervision.