Emotion-based Hierarchical Clustering of Romanian Poetry

Lupea, Mihaiela; Briciu, Anamaria; Bostenaru, Elena

doi:10.24846/v30i1y202110

Cited by 3 publications

(1 citation statement)

References 13 publications

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“…Pavaloi et al (2014) used three sets of recordings in Romanian language, annotated for positive and negative emotions, and trained models using k-NN and SVM classifiers. In terms of text data, Lupea et al (2021) present an unsupervised clustering approach used to mine emotional patterns in Mihai Eminescu's poetry, based on the Romanian Emotion Lexicon created by Lupea and Briciu (2019) for feature extraction. In terms of sentiment analysis for Romanian language, Istrati and Ciobotaru (2021) created a dataset of tweets annotated for positive/negative sentiment and trained several classifiers on it, both classical and modern.…”

Section: Recent Workmentioning

confidence: 99%

RED: A Novel Dataset for Romanian Emotion Detection from Tweets

Ciobotaru¹,

Dinu²

2021

Proceedings of the Conference Recent Advances in Natural Language Processing - Deep Learning for Natural Language Processing Me

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In Romanian language there are some resources for automatic text comprehension, but for Emotion Detection, not lexicon-based, there are none. To cover this gap, we extracted data from Twitter and created the first dataset containing tweets annotated with five types of emotions: joy, fear, sadness, anger and neutral, with the intent of being used for opinion mining and analysis tasks. In this article we present some features of our novel dataset, and create a benchmark to achieve the first supervised machine learning model for automatic Emotion Detection in Romanian short texts. We investigate the performance of four classical machine learning models: Multinomial Naive Bayes, Logistic Regression, Support Vector Classification and Linear Support Vector Classification. We also investigate more modern approaches like fastText, which makes use of subword information. Lastly, we finetune the Romanian BERT for text classification and our experiments show that the BERTbased model has the best performance for the task of Emotion Detection from Romanian tweets.

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Section: Recent Workmentioning

confidence: 99%

RED: A Novel Dataset for Romanian Emotion Detection from Tweets

Ciobotaru¹,

Dinu²

2021

Proceedings of the Conference Recent Advances in Natural Language Processing - Deep Learning for Natural Language Processing Me

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Clustering analysis of metered Arabic poetry compositions

Berkani,

Holzer

2023

2023 20th ACS/IEEE International Conference on Computer Systems and Applications (AICCSA)

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Towards Sentiment Analysis for Romanian Twitter Content

Neagu

Rus

Grec³

et al. 2022

Algorithms

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With the increased popularity of social media platforms such as Twitter or Facebook, sentiment analysis (SA) over the microblogging content becomes of crucial importance. The literature reports good results for well-resourced languages such as English, Spanish or German, but open research space still exists for underrepresented languages such as Romanian, where there is a lack of public training datasets or pretrained word embeddings. The majority of research on Romanian SA tackles the issue in a binary classification manner (positive vs. negative), using a single public dataset which consists of product reviews. In this paper, we respond to the need for a media surveillance project to possess a custom multinomial SA classifier for usage in a restrictive and specific production setup. We describe in detail how such a classifier was built, with the help of an English dataset (containing around 15,000 tweets) translated to Romanian with a public translation service. We test the most popular classification methods that could be applied to SA, including standard machine learning, deep learning and BERT. As we could not find any results for multinomial sentiment classification (positive, negative and neutral) in Romanian, we set two benchmark accuracies of ≈78% using standard machine learning and ≈81% using BERT. Furthermore, we demonstrate that the automatic translation service does not downgrade the learning performance by comparing the accuracies achieved by the models trained on the original dataset with the models trained on the translated data.

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Emotion-based Hierarchical Clustering of Romanian Poetry

Cited by 3 publications

References 13 publications

RED: A Novel Dataset for Romanian Emotion Detection from Tweets

RED: A Novel Dataset for Romanian Emotion Detection from Tweets

Clustering analysis of metered Arabic poetry compositions

Towards Sentiment Analysis for Romanian Twitter Content

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