• Corpus ID: 199448396

Overview of TASS 2019: One More Further for the Global Spanish Sentiment Analysis Corpus

@inproceedings{DazGaliano2019OverviewOT,
  title={Overview of TASS 2019: One More Further for the Global Spanish Sentiment Analysis Corpus},
  author={Manuel Carlos D{\'i}az-Galiano and Manuel Garc{\'i}a Vega and Edgar Casasola and Luis Chiruzzo and Miguel {\'A}ngel Garc{\'i}a Cumbreras and Eugenio Mart{\'i}nez-C{\'a}mara and Daniela Moctezuma and Arturo Montejo R{\'a}ez and Marco Antonio Sobrevilla Cabezudo and Eric Sadit Tellez and Mario Graff and Sabino Miranda-Jim{\'e}nez},
  booktitle={IberLEF@SEPLN},
  year={2019}
}
In September 2019, the eighth edition of TASS workshop (Task of Sentiment Analysis at SEPLN) was held in Bilbao, Spain as part of the first edition of IberLEF (Iberian Languages Evaluation Forum), which joined the efforts of the IberEval and TASS workshops. In this edition, the natural evolution from previous editions was proposed: sentiment analysis at tweet level. It includes two subtasks, monolingual and cross-lingual sentiment analysis, with different subsets of the InterTASS corpus (ES… 
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References

SHOWING 1-10 OF 11 REFERENCES
LaSTUS/TALN at TASS 2019: Sentiment Analysis for Spanish Language Variants with Neural Networks
TLDR
A deep learning approach based on bidirectional LSTM (biLSTM) models to face both sub-tasks of TASS, which focuses on the classification of tweets written in the Spanish language with respect to their polarity or sentiment.
Sentiment Analysis at SEPLN (TASS)-2019: Sentiment Analysis at Tweet Level Using Deep Learning
This paper describes the system submitted to "Sentiment Analysis at SEPLN (TASS)-2019" shared task. The task includes sentiment analysis of Spanish tweets, where the tweets are in different dialects
ELiRF-UPV at TASS 2019: Transformer Encoders for Twitter Sentiment Analysis in Spanish
TLDR
The participation of the ELiRF research group of the Universitat Politècnica de València in the TASS 2019 Workshop is described, focused mainly on employing the encoders of the Transformer model, based on self-attention mechanisms, achieving competitive results in the task addressed.
GTH-UPM at TASS 2019: Sentiment Analysis of Tweets for Spanish Variants
This article describes the system developed by the Grupo de Tecnoloǵıa del Habla at Universidad Politécnica de Madrid, Spain (GTH-UPM) for the competition on sentiment analysis in tweets: TASS 2019.
From Recurrency to Attention in Opinion Analysis: Comparing RNN vs Transformer Models
TLDR
Two different Deep Learning approaches are explored, validating their performance on both subtasks (Monolingual and Crosslingual Sentiment Analysis).
RETUYT-InCo at TASS 2019: Sentiment Analysis in Spanish Tweets
TLDR
Three approaches for classifying the sentiment of tweets for different Spanish variants in the TASS 2019 challenge are presented, based on Multilayer Perceptron (MLP), Long Short Term Memory networks (LSTM), and transfer learning using BERT.
TASS 2018: The Strength of Deep Learning in Language Understanding Tasks
TLDR
Two new tasks focused on semantic relation extraction in the health domain and emotion classification in the news domain were added to the two traditional tasks of TASS, namely sentiment analysis at tweet level and aspect level.
Atalaya at TASS 2019: Data Augmentation and Robust Embeddings for Sentiment Analysis
  • F. Luque
  • Computer Science
    IberLEF@SEPLN
  • 2019
TLDR
This article describes its participation in TASS 2019, a shared task aimed at the detection of sentiment polarity of Spanish tweets, and trained robust subword-aware word embeddings and computed tweet representations using a weighted-averaging strategy.
Atalaya at TASS 2018: Sentiment Analysis with Tweet Embeddings and Data Augmentation
TLDR
This work presents the participation as team Atalaya in the task of polarity classification of tweets, which followed standard techniques in preprocessing, representation and classification, and also explored some novel ideas.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
TLDR
A new language representation model, BERT, designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers, which can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks.
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2
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