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Role of Artificial Intelligence in Detection of Hateful Speech for Hinglish Data on Social Media [article]

Ananya Srivastava, Mohammed Hasan, Bhargav Yagnik, Rahee Walambe, Ketan Kotecha
2021 arXiv   pre-print
Social networking platforms provide a conduit to disseminate our ideas, views and thoughts and proliferate information. This has led to the amalgamation of English with natively spoken languages.  ...  Hate speech detection algorithms deployed by most social networking platforms are unable to filter out offensive and abusive content posted in these code-mixed languages.  ...  Enhanced approaches include changing the architecture of neural network classifier or using deeper/ multi-layer neural networks with larger corpus for Hindi-English code mix language.  ... 
arXiv:2105.04913v1 fatcat:e6fjcpiinfcznl4rxil3qrrbau

Towards Emotion Recognition in Hindi-English Code-Mixed Data: A Transformer Based Approach [article]

Anshul Wadhawan, Akshita Aggarwal
2021 arXiv   pre-print
Because of the availability of huge amounts of data from social-media, which is regularly used for expressing sentiments and opinions, this problem has garnered great attention.  ...  In this paper, we present a Hinglish dataset labelled for emotion detection.  ...  Creation of Hindi-English Bi-lingual Word Embeddings This being a multi-label text classification problem, it is required that the text be first converted to a form understandable by the various machine  ... 
arXiv:2102.09943v2 fatcat:wz5zwg2aybcuddbts4v32zmq4u

Machine Learning Techniques for Sentiment Analysis of Code-Mixed and Switched Indian Social Media Text Corpus - A Comprehensive Review

Gazi Imtiyaz Ahmad, Jimmy Singla, Anis Ali, Aijaz Ahmad Reshi, Anas A. Salameh
2022 International Journal of Advanced Computer Science and Applications  
A comprehensive review of sentiment analysis for code-mixed and switched text corpus of Indian social media using machine learning (ML) approaches, based on recent research studies has been presented in  ...  Sentiment analysis of monolingual social media content has been carried out for the last two decades.  ...  Word and character n-grams features were used and applied to SVM classifier for sentiment identification. Thus f-score of 0.569 were achieved for Hi-En and 0.526 for Bi-En datasets. B.  ... 
doi:10.14569/ijacsa.2022.0130254 fatcat:43ub7ku5xjeqvcjkpxfutpqgqi

Evaluation of different machine learning approaches and input text representations for multilingual classification of tweets for disease surveillance in the social web

Mark Abraham Magumba, Peter Nabende
2021 Journal of Big Data  
In this paper we experimentally examine different approaches for classifying text for epidemiological surveillance on the social web in addition we offer a systematic comparison of the impact of different  ...  AbstractTwitter and social media as a whole have great potential as a source of disease surveillance data however the general messiness of tweets presents several challenges for standard information extraction  ...  Acknowledgements We would like to acknowledge SNOMED-CT international for giving us free access to their ontology  ... 
doi:10.1186/s40537-021-00528-5 fatcat:markv4tcfjgnvdxxtcdfiw4zx4

A Rapid Review of Image Captioning

Adriyendi Adriyendi
2021 Journal of Information Technology and Computer Science  
We review image captioning into 4 categories based on input model, process model, output model, and lingual image caption. Input model is based on criteria caption, method, and dataset.  ...  Process model is based on type of learning, encoder-decoder, image extractor, and metric evaluation.  ...  BLEU (Bi-Lingual Evaluation Understudy) measures the same number of words as the base adverb [3] , which is used to measure the similarity between two sentences.  ... 
doi:10.25126/jitecs.202162316 fatcat:jebopkpe65gr3puusjzr4yzegy

Adversarial Deep Averaging Networks for Cross-Lingual Sentiment Classification [article]

Xilun Chen, Yu Sun, Ben Athiwaratkun, Claire Cardie, Kilian Weinberger
2018 arXiv   pre-print
ADAN has two discriminative branches: a sentiment classifier and an adversarial language discriminator.  ...  Unfortunately, most languages do not enjoy such an abundance of labeled data.  ...  Acknowledgments We thank the anonymous reviewers and members of Cornell NLP and ML groups for helpful comments. This work was funded in part by a grant from the DARPA Deft Program.  ... 
arXiv:1606.01614v5 fatcat:dmdxq6dazbcwparmn5lmts6cyq

Leveraging Large Amounts of Weakly Supervised Data for Multi-Language Sentiment Classification [article]

Jan Deriu, Aurelien Lucchi, Valeria De Luca, Aliaksei Severyn, Simon Müller, Mark Cieliebak, Thomas Hofmann, Martin Jaggi
2017 arXiv   pre-print
We leverage large amounts of weakly-supervised data in various languages to train a multi-layer convolutional network and demonstrate the importance of using pre-training of such networks.  ...  We thoroughly evaluate our approach on various multi-lingual datasets, including the recent SemEval-2016 sentiment prediction benchmark (Task 4), where we achieved state-of-the-art performance.  ...  networks to sentiment classification, distant supervision and training multi-lingual text classifiers.  ... 
arXiv:1703.02504v1 fatcat:4774tvngwvgv3nwndisxueas6e

Leveraging Large Amounts of Weakly Supervised Data for Multi-Language Sentiment Classification

Jan Deriu, Aurelien Lucchi, Valeria De Luca, Aliaksei Severyn, Simon Müller, Mark Cieliebak, Thomas Hofmann, Martin Jaggi
2017 Proceedings of the 26th International Conference on World Wide Web - WWW '17  
We leverage large amounts of weaklysupervised data in various languages to train a multi-layer convolutional network and demonstrate the importance of using pretraining of such networks.  ...  Previously proposed multi-lingual approaches typically require to establish a correspondence to English for which powerful classifiers are already available.  ...  networks to sentiment classification, distant supervision and training multi-lingual text classifiers.  ... 
doi:10.1145/3038912.3052611 dblp:conf/www/DeriuLLSMCHJ17 fatcat:vgb4afku3jbknht6g23wuhxvwu

Multilingual and cross-lingual document classification: A meta-learning approach [article]

Niels van der Heijden, Helen Yannakoudakis, Pushkar Mishra, Ekaterina Shutova
2021 arXiv   pre-print
on others, using only a small amount of labeled data.  ...  In this work, we propose a meta-learning approach to document classification in limited-resource setting and demonstrate its effectiveness in two different settings: few-shot, cross-lingual adaptation  ...  Each news story is manually classified into one of four groups: Corporate/Industrial, Economics, Government/Social and Markets.  ... 
arXiv:2101.11302v2 fatcat:36utmoigc5dx5dcjmif47av5he

A Distributed Representation-Based Framework for Cross-Lingual Transfer Parsing

Jiang Guo, Wanxiang Che, David Yarowsky, Haifeng Wang, Ting Liu
2016 The Journal of Artificial Intelligence Research  
This paper investigates the problem of cross-lingual transfer parsing, aiming at inducing dependency parsers for low-resource languages while using only training data from a resource-rich language (e.g  ...  Consequently, both lexical features and non-lexical features can be used in our model for cross-lingual transfer.  ...  This work was supported by the National Key Basic Research Program of China via grant 2014CB340503 and the National Natural Science Foundation of China (NSFC) via grant 61133012 and 61370164.  ... 
doi:10.1613/jair.4955 fatcat:mpkzaf774nabrbxq6u5n3lpxdu

A survey on sentiment analysis in Urdu: A resource-poor language

Asad Khattak, Muhammad Zubair Asghar, Anam Saeed, Ibrahim A. Hameed, Syed Asif Hassan, Shakeel Ahmad
2020 Egyptian Informatics Journal  
Published by Elsevier BV on behalf of Faculty of Computers and Artificial Intelligence, Cairo University.  ...  The primary goal of this study is to present state-of-art survey for identifying the progress and shortcomings saddling Urdu sentiment analysis and propose rectifications.  ...  This Research work was supported by Zayed University Research Incentives Fund#R18052, co-funded by Norwegian university of science and technology, Ålesund, Norway.  ... 
doi:10.1016/j.eij.2020.04.003 fatcat:qvymechpvnhypg2telxbs4wj4m

Automatic classification of sexism in social networks: an empirical study on Twitter data

Francisco Rodriguez-Sanchez, Jorge Carrillo-de-Albornoz, Laura Plaza
2020 IEEE Access  
Our results show that sexism is frequently found in many forms in social networks, that it includes a wide range of behaviours, and that it is possible to detect them using deep learning approaches.  ...  To this end, we have developed and released the first dataset of sexist expressions and attitudes in Twitter in Spanish (MeTwo) and investigate the feasibility of using machine learning techniques (both  ...  The dataset is accompanied by an exhaustive study and categorization of frequent sexist expressions in social networks. III.  ... 
doi:10.1109/access.2020.3042604 fatcat:yaucfvtnrjfzjlnmn7rzzvgta4

Detecting Offensive Tweets in Hindi-English Code-Switched Language

Puneet Mathur, Rajiv Shah, Ramit Sawhney, Debanjan Mahata
2018 Proceedings of the Sixth International Workshop on Natural Language Processing for Social Media  
Further, we approach the problem of classification of the tweets in HEOT dataset using transfer learning wherein the proposed model employing Convolutional Neural Networks is pre-trained on tweets in English  ...  The exponential rise of social media websites like Twitter, Facebook and Reddit in linguistically diverse geographical regions has led to hybridization of popular native languages with English in an effort  ...  Hinglish is a pronunciation based bi-lingual language that has no fixed grammar rules.  ... 
doi:10.18653/v1/w18-3504 dblp:conf/acl-socialnlp/MathurSSM18 fatcat:jflht7hdprevxk57mobylfvpuu

An Attention Based Neural Network for Code Switching Detection: English Roman Urdu [article]

Aizaz Hussain, Muhammad Umair Arshad
2021 arXiv   pre-print
Code-switching is a common phenomenon among people with diverse lingual background and is widely used on the internet for communication purposes.  ...  The attention model enables the architecture to learn the important features of the languages hence classifying the code switched data.  ...  In the decoder part, the values from the attention layer are passed into the recurrent network thus learning the sequence of the word & classifying it.  ... 
arXiv:2103.02252v1 fatcat:mrlacjcb6nc6dpabm5ipok2hrq

A Review on Text-Based Emotion Detection – Techniques, Applications, Datasets, and Future Directions [article]

Sheetal Kusal, Shruti Patil, Jyoti Choudrie, Ketan Kotecha, Deepali Vora, Ilias Pappas
2022 arXiv   pre-print
It also reviews the different applications of TBED across various research domains and highlights its use.  ...  An overview of various emotion models, techniques, feature extraction methods, datasets, and research challenges with future directions has also been represented.  ...  A Bi-GRU network that is bi-directional is a variant of GRU (Bi-GRU).  ... 
arXiv:2205.03235v1 fatcat:b3m25fg6xfc3leeym22eqysq5a
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