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Emoji-Powered Representation Learning for Cross-Lingual Sentiment Classification (Extended Abstract)

Zhenpeng Chen, Sheng Shen, Ziniu Hu, Xuan Lu, Qiaozhu Mei, Xuanzhe Liu
2020 Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence  
The learned representations are then integrated to facilitate cross-lingual sentiment classification.  ...  We propose a novel representation learning method that uses emoji prediction as an instrument to learn respective sentiment-aware representations for each language.  ...  As a result, RAT learns more representative sequential features for PS. Transformer is a powerful attention model primarily used in the field of NLP [Vaswani et al., 2017] .  ... 
doi:10.24963/ijcai.2020/641 dblp:conf/ijcai/XuZYZT20 fatcat:jpvf4glchvb3zaymedyehejjvq

Bayesian Case-Exclusion and Personalized Explanations for Sustainable Dairy Farming (Extended Abstract)

Eoin M. Kenny, Elodie Ruelle, Anne Geoghegan, Laurence Shalloo, Micheál O'Leary, Michael O'Donovan, Mohammed Temraz, Mark T. Keane
2020 Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence  
PBI-CBR's key novelty is its use of Bayesian methods for case-base maintenance in a regression domain.  ...  Smart agriculture (SmartAg) has emerged as a rich domain for AI-driven decision support systems (DSS); however, it is often challenged by user-adoption issues.  ...  cross-lingual sentiment classification.  ... 
doi:10.24963/ijcai.2020/649 dblp:conf/ijcai/ChenSHLML20 fatcat:cjn73bwpqzdcheoctkkl62fokq

Sentiment analysis using deep learning approaches: an overview

Olivier Habimana, Yuhua Li, Ruixuan Li, Xiwu Gu, Ge Yu
2019 Science China Information Sciences  
The main benefit of machine learning approaches is their ability of representation learning. Pang et al. [8] pioneered the use of these techniques for sentiment analysis.  ...  Machine learning approaches are other traditional methods for sentiment analysis that are based on the machine learning algorithms to classify the words into their corresponding sentiment labels.  ...  [66] suggested a word embedding model that works in a cross-lingual setting to solve the problem of the semantic gap between English-Chinese for sentiment classification. Besides, Fu et al.  ... 
doi:10.1007/s11432-018-9941-6 fatcat:nbevrfiyybhszirol2af26c6ve

A Survey on Visual Sentiment Analysis

Alessandro Ortis, Giovanni Maria Farinella, Sebastiano Battiato
2020 IET Image Processing  
To this aim, this paper considers a structured formalization of the problem which is usually used for the analysis of text, and discusses it's suitability in the context of Visual Sentiment Analysis.  ...  Although this field is rather new, a broad range of techniques have been developed for various data sources and problems, resulting in a large body of research.  ...  [44] extended the ANP ontology defined in [32] for a multi-lingual context. Specifically, the method provides a multilingual sentiment driven visual concept detector in 12 languages.  ... 
doi:10.1049/iet-ipr.2019.1270 fatcat:cuhaluxac5ar5ky4rkoqmug6x4

Cross-lingual Hate Speech Detection using Transformer Models [article]

Teodor Tiţa, Arkaitz Zubiaga
2021 arXiv   pre-print
Hate speech detection within a cross-lingual setting represents a paramount area of interest for all medium and large-scale online platforms.  ...  This paper illustrates the capabilities of fine-tuned altered multi-lingual Transformer models (mBERT, XLM-RoBERTa) regarding this crucial social data science task with cross-lingual training from English  ...  Deep Learning Approaches These methods employ deep neural networks such that abstract feature representations could be learned from inputted data.  ... 
arXiv:2111.00981v1 fatcat:jvo6ad5bevbjxac46ws3ku5r7a


Huyen Trang Phan, Ngoc Thanh Nguyen, Dosam Hwang
2021 Journal of Computer Science and Cybernetics  
Sentiments from opinions are a valuable data source for solving many issues. Therefore, sentiment analysis has developed into one of the most popular natural language processing fields.  ...  This survey presents a summary of the necessary stages for building a complete model to be used in sentiment analysis.  ...  Easily change graphs structure when data change Tough to train graphs in solving the problems Multi-view learning Can handle problems of cross-lingual and various linguistic Fail for language with  ... 
doi:10.15625/1813-9663/37/4/15892 fatcat:2dgv3sygovgelk3mffmvnmokay

Modeling Profanity and Hate Speech in Social Media with Semantic Subspaces [article]

Vanessa Hahn, Dana Ruiter, Thomas Kleinbauer, Dietrich Klakow
2021 arXiv   pre-print
, with improvements between F1 +10.9 and F1 +42.9 over the baselines across all tested monolingual and cross-lingual scenarios.  ...  This is done monolingually (German) and cross-lingually to closely-related (English), distantly-related (French) and non-related (Arabic) tasks.  ...  Responsibility for the content of this publication is with the authors.  ... 
arXiv:2106.07505v2 fatcat:ai2mr22lq5crlouhdztuaimzxm

Deep Learning for Sentiment Analysis : A Survey [article]

Lei Zhang, Shuai Wang, Bing Liu
2018 arXiv   pre-print
Deep learning has emerged as a powerful machine learning technique that learns multiple layers of representations or features of the data and produces state-of-the-art prediction results.  ...  Along with the success of deep learning in many other application domains, deep learning is also popularly used in sentiment analysis in recent years.  ...  Zhou et al. 46 designed an attention-based LSTM network for cross-lingual sentiment classification at the document level.  ... 
arXiv:1801.07883v2 fatcat:nplicfgaozb6fbfx4eyts4zt7e

A Comprehensive Survey on Word Representation Models: From Classical to State-Of-The-Art Word Representation Language Models [article]

Usman Naseem, Imran Razzak, Shah Khalid Khan, Mukesh Prasad
2020 arXiv   pre-print
Further, such representations can be utilized by various machine learning (ML) algorithms for a variety of NLP related tasks.  ...  In this survey, we explore different word representation models and its power of expression, from the classical to modern-day state-of-the-art word representation language models (LMS).  ...  Cross-lingual LM.  ... 
arXiv:2010.15036v1 fatcat:ykwirbx7afhyxm7dxkzrfpaslm

Over a Decade of Social Opinion Mining [article]

Keith Cortis, Brian Davis
2020 arXiv   pre-print
Social media popularity and importance is on the increase, due to people using it for various types of social interaction across multiple channels.  ...  This systematic review focuses on the evolving research area of Social Opinion Mining, tasked with the identification of multiple opinion dimensions, such as subjectivity, sentiment polarity, emotion,  ...  Therefore, it provides generalpurpose architectures, such as BERT, GPT-2 [631], RoBERTa, cross-lingual language model (XLM) [632], DistilBert [633], and XLNET [634] for NLP tasks (like sentiment analysis  ... 
arXiv:2012.03091v1 fatcat:bm5nydbdvbalzi33l3w2ivkdja

State of Art for Semantic Analysis of Natural Language Processing

Dastan Hussen Maulud, Subhi R. M. Zeebaree, Karwan Jacksi, Mohammed A. Mohammed Sadeeq, Karzan Hussein Sharif
2021 Qubahan Academic Journal  
The findings suggest that the best-achieved accuracy of checked papers and those who relied on the Sentiment Analysis approach and the prediction error is minimal.  ...  Research that compares the cross-lingual comparative analysis of 76 different languages was conducted.  ...  Five separate machine learning models are employed to construct classifiers for sentiment analysis.  ... 
doi:10.48161/qaj.v1n2a44 fatcat:ie7ddgixprhzffxkyuklkdktzy

CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLP [article]

Qinyuan Ye, Bill Yuchen Lin, Xiang Ren
2021 arXiv   pre-print
We introduce CrossFit, a problem setup for studying cross-task generalization ability, which standardizes seen/unseen task partitions, data access during different learning stages, and the evaluation protocols  ...  Humans can learn a new language task efficiently with only few examples, by leveraging their knowledge obtained when learning prior tasks.  ...  We thank huggingface datasets team for making datasets more accessible. We thank anonymous reviewers and members of USC  ... 
arXiv:2104.08835v2 fatcat:xnhrmmsmyzb4fjo7ealrw2vnka

Hope Speech detection in under-resourced Kannada language [article]

Adeep Hande, Ruba Priyadharshini, Anbukkarasi Sampath, Kingston Pal Thamburaj, Prabakaran Chandran, Bharathi Raja Chakravarthi
2021 arXiv   pre-print
In addition, we introduce DC-BERT4HOPE, a dual-channel model that uses the English translation of KanHope for additional training to promote hope speech detection.  ...  Hossain, E., Sharif, O., Hoque, M.M.: NLP-CUET@LT-EDI-EACL2021: Multilingual code-mixed hope speech detection using cross-lingual representation learner.  ...  We observe that this model performs poorer than DC-BERT4HOPE (bert-mbert), despite XLM- RoBERTa being pretrained on 2.5 TB of data and its approach to an unsupervised cross-lingual learning scale.  ... 
arXiv:2108.04616v2 fatcat:mxawtdglxbgmflsmjers6htdja

NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation [article]

Kaustubh D. Dhole, Varun Gangal, Sebastian Gehrmann, Aadesh Gupta, Zhenhao Li, Saad Mahamood, Abinaya Mahendiran, Simon Mille, Ashish Srivastava, Samson Tan, Tongshuang Wu, Jascha Sohl-Dickstein (+113 others)
2021 arXiv   pre-print
We describe the framework and an initial set of 117 transformations and 23 filters for a variety of natural language tasks.  ...  Learning word vectors for sentiment analysis.  ...  and cross-lingual set- week ) woche tings.  ... 
arXiv:2112.02721v1 fatcat:uqizuxc4wzgxnnfsc6azh6ckpq

Predicting the pandemic: sentiment evaluation and predictive analysis from large-scale tweets on Covid-19 by deep convolutional neural network

Sourav Das, Anup Kumar Kolya
2021 Evolutionary Intelligence  
Textual sentiment classification harnesses the full computational potential of deep learning models.  ...  Engaging deep neural networks for textual sentiment analysis is an extensively practiced domain of research.  ...  Also, for cross-lingual vocabulary, two similar identical words can lead up to way different meanings.  ... 
doi:10.1007/s12065-021-00598-7 pmid:33815622 pmcid:PMC8007226 fatcat:rrijxtbe65aiflbf2ccxqlklsm
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