Published on Mon Jul 27 2020

[email protected] at SemEval-2020 Task 9: Generative Morphemes with an Attention Model for Sentiment Analysis in Code-Mixed Text

Koustava Goswami, Priya Rani, Bharathi Raja Chakravarthi, Theodorus Fransen, John P. McCrae

Code mixing is a common phenomena in multilingual societies where people switch from one language to another. The system aims to predict the sentiments of the given English-Hindi code-mixed tweets without using word-level language tags.

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Abstract

Code mixing is a common phenomena in multilingual societies where people switch from one language to another for various reasons. Recent advances in public communication over different social media sites have led to an increase in the frequency of code-mixed usage in written language. In this paper, we present the Generative Morphemes with Attention (GenMA) Model sentiment analysis system contributed to SemEval 2020 Task 9 SentiMix. The system aims to predict the sentiments of the given English-Hindi code-mixed tweets without using word-level language tags instead inferring this automatically using a morphological model. The system is based on a novel deep neural network (DNN) architecture, which has outperformed the baseline F1-score on the test data-set as well as the validation data-set. Our results can be found under the user name "koustava" on the "Sentimix Hindi English" page

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BAKSA at SemEval-2020 Task 9: Bolstering CNN with Self-Attention for Sentiment Analysis of Code Mixed Text
Sentiment Analysis of code-mixed text has diversified applications in opinion mining. We present an ensemble architecture of convolutional neural net (CNN) and self-attention based LSTM for sentiment analysis. We achieved F1 scores of 0.707 (ranked 5th)
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Fri Jul 24 2020
NLP
IUST at SemEval-2020 Task 9: Sentiment Analysis for Code-Mixed Social Media Text using Deep Neural Networks and Linear Baselines
Sentiment Analysis is a well-studied field of Natural Language Processing. The rapid growth of social media and noisy content within them poses significant challenges. One of these challenges is code-mixing, which means using different languages.
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Sat Oct 10 2020
Artificial Intelligence
HPCC-YNU at SemEval-2020 Task 9: A Bilingual Vector Gating Mechanism for Sentiment Analysis of Code-Mixed Text
Code-mixed text poses a great challenge for the traditional NLP system. The proposed system outperforms the baseline algorithm. We achieved fifth place in Spanglish and 19th place in Hinglish.
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Mon Sep 07 2020
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NLP-CIC at SemEval-2020 Task 9: Analysing sentiment in code-switching language using a simple deep-learning classifier
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Sun Aug 30 2020
Artificial Intelligence
LIMSI_UPV at SemEval-2020 Task 9: Recurrent Convolutional Neural Network for Code-mixed Sentiment Analysis
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Fri Jan 22 2021
Machine Learning
[email protected]: A Meta Embedding and Transformer model for Code-Mixed Sentiment Analysis on Social Media Text
Code-mixing adds a challenge to sentiment analysis due to its non-standard representations. This paper proposes a meta embedding with a transformer method for sentiment analysis on the Dravidian code-mixed dataset. The code is provided in the Github repo.
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