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Emoji-Powered Representation Learning for Cross-Lingual Sentiment Classification [article]

Zhenpeng Chen and Sheng Shen and Ziniu Hu and Xuan Lu and Qiaozhu Mei and Xuanzhe Liu
2019 arXiv   pre-print
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.  ...  We propose ELSA, a novel framework of Emoji-powered representation learning for cross-Lingual Sentiment Analysis.  ... 
arXiv:1806.02557v2 fatcat:52k23nlb4vdgxksjupmhyxajd4

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

Learning Cross-lingual Embeddings from Twitter via Distant Supervision [article]

Jose Camacho-Collados, Yerai Doval, Eugenio Martínez-Cámara, Luis Espinosa-Anke, Francesco Barbieri, Steven Schockaert
2020 arXiv   pre-print
In this paper we explore a research direction that has been surprisingly neglected in the literature: leveraging noisy user-generated text to learn cross-lingual embeddings particularly tailored towards  ...  Cross-lingual embeddings represent the meaning of words from different languages in the same vector space.  ...  Finally, we would like to thank Taher Pilehvar for his help with the Farsi qualitative analysis.  ... 
arXiv:1905.07358v3 fatcat:lq7qxn5rw5cttpllhn67a3vjea

Emoji-aware Co-attention Network with EmoGraph2vec Model for Sentiment Anaylsis [article]

Xiaowei Yuan, Jingyuan Hu, Xiaodan Zhang, Honglei Lv, Hao Liu
2022 arXiv   pre-print
As for the sentiment analysis task, many researchers ignore the emotional impact of the interaction between text and emojis.  ...  In this work, we propose a method to learn emoji representations called EmoGraph2vec and design an emoji-aware co-attention network that learns the mutual emotional semantics between text and emojis on  ...  [26] proposed a novel representation learning method that uses emoji prediction as an instrument to learn respective sentiment-aware representations for different languages.  ... 
arXiv:2110.14636v2 fatcat:tyi6pb2yafdcrlddqhqys7k5cu

Cross-Lingual Text Classification with Minimal Resources by Transferring a Sparse Teacher [article]

Giannis Karamanolakis, Daniel Hsu, Luis Gravano
2020 arXiv   pre-print
Existing approaches for transferring supervision across languages require expensive cross-lingual resources, such as parallel corpora, while less expensive cross-lingual representation learning approaches  ...  Cross-lingual text classification alleviates the need for manually labeled documents in a target language by leveraging labeled documents from other languages.  ...  Acknowledgments We thank the anonymous reviewers for their constructive feedback. This material is based upon work supported by the National Science Foundation under Grant No. IIS-15-63785.  ... 
arXiv:2010.02562v1 fatcat:rggtsno3i5fcnnhzobzl6vevmq

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.  ...  By employing such representation methods image features are learned from the data.  ... 
doi:10.1049/iet-ipr.2019.1270 fatcat:cuhaluxac5ar5ky4rkoqmug6x4

UPB at SemEval-2020 Task 9: Identifying Sentiment in Code-Mixed Social Media Texts using Transformers and Multi-Task Learning [article]

George-Eduard Zaharia, George-Alexandru Vlad, Dumitru-Clementin Cercel, Traian Rebedea, Costin-Gabriel Chiru
2020 arXiv   pre-print
Sentiment analysis is a process widely used in opinion mining campaigns conducted today.  ...  By interleaving words from two languages, the user can express with ease, but at the cost of making the text far less intelligible for those who are not familiar with this technique, but also for standard  ...  Question Answering, or Cross-lingual Natural Language Inference.  ... 
arXiv:2009.02780v1 fatcat:w5mnmvawurdnxe7ekjkv2lhxua

Learning from the ubiquitous language

Xuan Lu, Wei Ai, Xuanzhe Liu, Qian Li, Ning Wang, Gang Huang, Qiaozhu Mei
2016 Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing - UbiComp '16  
We demonstrate that the categories and frequencies of emojis used by these users provide rich signals for the identification and the understanding of cultural differences of smartphone users.  ...  We protect user privacy by removing all user identifiers and textual content of user input other than emojis. Please contact the corresponding author Xuanzhe Liu ( for more information.  ...  The accuracy of the state-of-the-art, bi-class sentiment classification is widely believed to be around 80-85% .  ... 
doi:10.1145/2971648.2971724 dblp:conf/huc/LuALLW0M16 fatcat:zj5s4o2zunabbkdj72j6gbhsiq

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  ...  The core challenge of cross-lingual hate speech detection resides in the very nature of it being a cross-lingual task.  ... 
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 Paradigm with Transformed Monolingual Word Embeddings for Multilingual Sentiment Analysis [article]

Yujie Lu, Tatsunori Mori
2017 arXiv   pre-print
In this paper, we propose a new deep learning paradigm to assimilate the differences between languages for MSA.  ...  The surge of social media use brings huge demand of multilingual sentiment analysis (MSA) for unveiling cultural difference.  ...  Our paradigm provides a great cross-lingual adaptability.  ... 
arXiv:1710.03203v2 fatcat:2uwd5egwlfg5tp76ypwggw4z5i

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
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