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A Deep Learning Approach towards Cross-Lingual Tweet Tagging

Nikhil Bharadwaj Gosala, Shalini Chaudhuri, Monica Adusumilli, Kartik Sethi
2016 Forum for Information Retrieval Evaluation  
Our model aims to extract the named entities from tweets using a Recurrent Neural Network Core.  ...  Long Short Term Memory (LSTM) was used to learn long term dependencies in our supervised learning model.  ...  RNN Core Upon studying various models for NER tagging, Deep Learning and especially Recurrent Neural Networks (RNNs) was chosen for the task of Tweet Tagging.  ... 
dblp:conf/fire/GosalaCAS16 fatcat:72bnizqoafcjjbfeu5isut3ciu

NLP-CIC at SemEval-2020 Task 9: Analysing sentiment in code-switching language using a simple deep-learning classifier [article]

Jason Angel, Segun Taofeek Aroyehun, Antonio Tamayo, Alexander Gelbukh
2020 arXiv   pre-print
Our simple approach achieved a F1-score of 0.71 on test set on the competition.  ...  In this paper, we use a standard convolutional neural network model to predict the sentiment of tweets in a blend of Spanish and English languages.  ...  These representations can be used to encode inputs for deep learning models. The effectiveness of these text representation approaches on code-switched texts remains an open question.  ... 
arXiv:2009.03397v1 fatcat:zdsw3ft6lzff7ldcdqmgaa3pki

Covhindia: Deep Learning Framework for Sentiment Polarity Detection of Covid-19 Tweets in Hindi

Purva Singh
2020 International Journal on Natural Language Computing  
This paper proposes a framework, Covhindia, a deep-learning framework that performs sentiment polarity detection of tweets related to COVID-19 posted in the Hindi language on the Twitter platform.  ...  The proposed framework leverages machine translation on Hindi tweets and passes the translated data as input to a deep learning model which is trained on an English corpus of COVID-19 tweets posted from  ...  METHODOLOGY Data Preprocessing As the first step towards sentiment classification of Hindi tweets, the proposed framework trains a deep learning model for detecting tweets' sentiment polarity in English  ... 
doi:10.5121/ijnlc.2020.9502 fatcat:5oua23zvrzf6vhughnrwtdu5za

L3CubeMahaSent: A Marathi Tweet-based Sentiment Analysis Dataset [article]

Atharva Kulkarni, Meet Mandhane, Manali Likhitkar, Gayatri Kshirsagar, Raviraj Joshi
2021 arXiv   pre-print
Finally, we present the statistics of our dataset and baseline classification results using CNN, LSTM, ULMFiT, and BERT-based deep learning models.  ...  Popular languages like English, Arabic, Russian, Mandarin, and also Indian languages such as Hindi, Bengali, Tamil have seen a significant amount of work in this area.  ...  We would like to express our gratitude towards our mentors at L3Cube for their continuous support and encouragement.  ... 
arXiv:2103.11408v2 fatcat:bycabla2mbfolonfcfmit5ezsu

Developing Cross-lingual Sentiment Analysis of Malay Twitter Data Using Lexicon-based Approach

Nur Imanina Zabha, Zakiah Ayop, Syarulnaziah Anawar, Erman Hamid, Zaheera Zainal
2019 International Journal of Advanced Computer Science and Applications  
The objective of this study was to develop a cross-lingual sentiment analysis using lexicon based approach.  ...  Sentiment analysis is a process of detecting and classifying sentiments into positive, negative or neutral. Most sentiment analysis research focus on English lexicon vocabularies.  ...  ACKNOWLEDGMENT We would like to express a deep gratitude to the Center of Advanced Computing Technology (C-ACT), Fakulti Teknologi Maklumat dan Komunikasi, Universiti Teknikal Malaysia Melaka (UTeM) for  ... 
doi:10.14569/ijacsa.2019.0100146 fatcat:3xir446kb5ghherxdjcreh62dy

Sentiment Analysis Using Deep Learning Techniques: A Review

Qurat Tul, Mubashir Ali, Amna Riaz, Amna Noureen, Muhammad Kamranz, Babar Hayat, A. Rehman
2017 International Journal of Advanced Computer Science and Applications  
of sentiment analysis such as sentiment classification, cross lingual problems, textual and visual analysis and product review analysis, etc.  ...  And to solve this issue, the sentiment analysis and deep learning techniques have been merged because deep learning models are effective due to their automatic learning capability.  ...  of cross lingual sentiment classification than the previous studies.  ... 
doi:10.14569/ijacsa.2017.080657 fatcat:us4hwclsx5ghtjo4v5vkvfkqqm

GTH-UPM at TASS 2019: Sentiment Analysis of Tweets for Spanish Variants

Ignacio González Godino, Luis Fernando D'Haro
2019 Annual Conference of the Spanish Society for Natural Language Processing  
The developed system consisted of three classifiers: a) a system based on feature vectors extracted from the tweets, b) a neural-based classifier using FastText, and c) a deep neural network classifier  ...  GTH-UPM) for the competition on sentiment analysis in tweets: TASS 2019.  ...  In the cross-lingual setting, in order to test the dependency of systems on a variant, they could be trained in a selection of any variant except the one which was used to test.  ... 
dblp:conf/sepln/GodinoD19 fatcat:z7betqkbkzcddkqvrmcgmesry4

Transfer Learning for Mining Feature Requests and Bug Reports from Tweets and App Store Reviews [article]

Pablo Restrepo Henao, Jannik Fischbach, Dominik Spies, Julian Frattini, Andreas Vogelsang
2021 arXiv   pre-print
Existing approaches build on traditional machine learning (ML) and deep learning (DL), but fail to detect feature requests and bug reports with high Recall and acceptable Precision which is necessary for  ...  We found that monolingual BERT models outperform existing baseline methods in the classification of English App Reviews as well as English and Italian Tweets.  ...  So far, other studies on cross-lingual transfer learning have mainly achieved good results for token-level prediction (e.g., POS tagging [16] ).  ... 
arXiv:2108.00663v1 fatcat:kh4d7hizybe2rbqws4o22osdp4

Semantic Sentiment Analysis of Twitter Data [article]

Preslav Nakov
2017 arXiv   pre-print
This has resulted in the proliferation of social media content, thus creating new opportunities to study public opinion at a scale that was never possible before.  ...  Finally, as methods for sentiment analysis mature, more attention is also being paid to linguistic structure and to multi-linguality and cross-linguality.  ...  using a deep neural network.  ... 
arXiv:1710.01492v1 fatcat:a7jaelezevhexfuh4qul5nvdzy

A Survey of Code-switched Speech and Language Processing [article]

Sunayana Sitaram, Khyathi Raghavi Chandu, Sai Krishna Rallabandi, Alan W Black
2020 arXiv   pre-print
This survey reviews computational approaches for code-switched Speech and Natural Language Processing.  ...  Code-switching, the alternation of languages within a conversation or utterance, is a common communicative phenomenon that occurs in multilingual communities across the world.  ...  In [159] authors present an approach using multi task learning by jointly learning language modeling as well POS tagging. [160] use a bilingual attention language model that learns cross-lingual probabilities  ... 
arXiv:1904.00784v3 fatcat:r5tsg4kdnfbtnndae523c32pta

Embedding Projection for Targeted Cross-lingual Sentiment: Model Comparisons and a Real-World Study

Jeremy Barnes, Roman Klinger
2019 The Journal of Artificial Intelligence Research  
To improve this situation, we propose a cross-lingual approach to sentiment analysis that is applicable to under-resourced languages and takes into account target-level information.  ...  Sentiment analysis benefits from large, hand-annotated resources in order to train and test machine learning models, which are often data hungry.  ...  This work has been partially supported by the DFG Collaborative Research Centre SFB 732, a SGR-DTCL Predoctoral Scholarship, and by the German Research Council (DFG) project SEAT (Structured Multi-Domain  ... 
doi:10.1613/jair.1.11561 fatcat:yphxy5fqdzfbrehqzgjpnbmmfy

No Rumours Please! A Multi-Indic-Lingual Approach for COVID Fake-Tweet Detection [article]

Debanjana Kar, Mohit Bhardwaj, Suranjana Samanta, Amar Prakash Azad
2020 arXiv   pre-print
We also propose a zero-shot learning approach to alleviate the data scarcity issue for such low resource languages.  ...  Towards this, we propose an approach to detect fake news about COVID-19 early on from social media, such as tweets, for multiple Indic-Languages besides English.  ...  We show that model trained with multiple Indic-Languages (our Indic-covidemic tweet dataset) fake news dataset tweets shows improved performance which can be attributed to cross-lingual transfer learning  ... 
arXiv:2010.06906v1 fatcat:fyfih46qcrh3teazrf6dahtlhq

Cross-Lingual Classification of Crisis Data [chapter]

Prashant Khare, Grégoire Burel, Diana Maynard, Harith Alani
2018 Lecture Notes in Computer Science  
In this paper we test statistical and semantic classification approaches on cross-lingual datasets from 30 crisis events, consisting of posts written mainly in English, Spanish, and Italian.  ...  We show that the addition of semantic features extracted from external knowledge bases improve accuracy over a purely statistical model.  ...  Methods for this kind of classification use a variety of supervised machine learning approaches, usually relying on linguistic and statistical features such as POS tags, user mentions, post length, and  ... 
doi:10.1007/978-3-030-00671-6_36 fatcat:dpey5famdfdn7ksfxulelazisu

"Did you really mean what you said?" : Sarcasm Detection in Hindi-English Code-Mixed Data using Bilingual Word Embeddings [article]

Akshita Aggarwal, Anshul Wadhawan, Anshima Chaudhary, Kavita Maurya
2020 arXiv   pre-print
We propose a deep learning based approach to address the issue of sarcasm detection in Hindi-English code mixed tweets using bilingual word embeddings derived from FastText and Word2Vec approaches.  ...  We present a corpus of tweets for training custom word embeddings and a Hinglish dataset labelled for sarcasm detection.  ...  Having a class-balanced dataset was significant to our problem to ensure that deep-learning models learn the right trends, not being biased towards a particular class.  ... 
arXiv:2010.00310v3 fatcat:cpwc7vm6ffajrn5b34zso5427y

Sentiment Polarity Detection in Bengali Tweets Using Deep Convolutional Neural Networks

Kamal Sarkar
2018 Journal of Intelligent Systems  
In this paper, we present an approach that classifies the sentiment polarity of Bengali tweets using deep neural networks which consist of one convolutional layer, one hidden layer and one output layer  ...  Our proposed approach has been tested on the Bengali tweet dataset released for Sentiment Analysis in Indian Languages contest 2015.  ...  [3] proposed several approaches to cross-lingual subjectivity analysis by directly applying the translations of opinion corpus in English to training an opinion classifier in Romanian and Spanish.  ... 
doi:10.1515/jisys-2017-0418 fatcat:roclep3lzbfnhio4bpbnxajb7e
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