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Identifying tweets of personal health experience through word embedding and LSTM neural network

Keyuan Jiang, Shichao Feng, Qunhao Song, Ricardo A. Calix, Matrika Gupta, Gordon R. Bernard
2018 BMC Bioinformatics  
Conclusion: We presented an efficient and effective method of identifying health-related personal experience tweets by combining word embedding and an LSTM neural network.  ...  Through word embedding, tweet texts were represented as dense vectors which in turn were fed to the LSTM neural network as sequences.  ...  Funding This work and publication of this article were supported by the National Institutes of Health Grant 1R15LM011999-01.  ... 
doi:10.1186/s12859-018-2198-y pmid:29897323 pmcid:PMC5998756 fatcat:ehqfugt3effavn2boitlqx45tu

Public Perception Analysis of Tweets During the 2015 Measles Outbreak: Comparative Study Using Convolutional Neural Network Models

Jingcheng Du, Lu Tang, Yang Xiang, Degui Zhi, Jun Xu, Hsing-Yi Song, Cui Tao
2018 Journal of Medical Internet Research  
We compared the performance of the CNN models to those of 4 conventional machine learning models and another neural network model.  ...  We also compared the impact of different word embeddings configurations for the CNN models: (1) Stanford GloVe embedding trained on billions of tweets in the general domain, (2) measles-specific embedding  ...  Diseases of the National Institutes of Health under award number R01AI130460, and the UTHealth Innovation for Cancer Prevention Research Training Program Pre-Doctoral Fellowship (Cancer Prevention and  ... 
doi:10.2196/jmir.9413 pmid:29986843 pmcid:PMC6056740 fatcat:fd6aznkjarbhbpolfaas4gwo6y

Ontology-Based Healthcare Named Entity Recognition from Twitter Messages Using a Recurrent Neural Network Approach

Erdenebileg Batbaatar, Keun Ho Ryu
2019 International Journal of Environmental Research and Public Health  
Analyzing user messages in social media networks such as Twitter can provide opportunities to detect and manage public health events.  ...  A bidirectional long short-term memory (BiLSTM) model learned rich context information, and a convolutional neural network (CNN) was used to produce character-level features.  ...  BiLSTM Recurrent neural networks (RNNs) [67] are a family of neural networks.  ... 
doi:10.3390/ijerph16193628 pmid:31569654 pmcid:PMC6801946 fatcat:lpyxnifejzbj5cyuxnlxrgz3am

SmokEng: Towards Fine-grained Classification of Tobacco-related Social Media Text [article]

Kartikey Pant, Venkata Himakar Yanamandra, Alok Debnath, Radhika Mamidi
2019 arXiv   pre-print
In this paper, we create a dataset of 3144 tweets, which are selected based on the presence of colloquial slang related to smoking and analyze it based on the semantics of the tweet.  ...  Contemporary datasets on tobacco consumption focus on one of two topics, either public health mentions and disease surveillance, or sentiment analysis on topical tobacco products and services.  ...  also focuses on public health surveillance, and uses word embeddings on a topic classifier in order to identify and capture semantic similarities between medical tweets by disease and tweet type for a  ... 
arXiv:1910.05598v1 fatcat:vqb72ttn7rgppf7k7frgsm57za

#phramacovigilance - Exploring Deep Learning Techniques for Identifying Mentions of Medication Intake from Twitter [article]

Debanjan Mahata, Jasper Friedrichs, Hitkul, Rajiv Ratn Shah
2018 arXiv   pre-print
Towards this objective, we train different deep neural network classification models on a publicly available annotated dataset and study their performances on identifying mentions of personal intake of  ...  We also design and train a new architecture of a stacked ensemble of shallow convolutional neural network (CNN) ensembles.  ...  Bi-directional LSTM Recurrent Neural Network -A Recurrent Neural Network (RNN) (Elman, 1990) is a neural network architecture that can process sequences of arbitrary length.  ... 
arXiv:1805.06375v1 fatcat:jstwq5z4rbg77j6utpthj2kqsy

Ontology-driven aspect-based sentiment analysis classification: An infodemiological case study regarding infectious diseases in Latin America

José Antonio García-Díaz, Mar Cánovas-García, Rafael Valencia-García
2020 Future generations computer systems  
Infodemiology is the process of mining unstructured and textual data so as to provide public health officials and policymakers with valuable information regarding public health.  ...  tweets concerning the Zika, Dengue and Chikungunya viruses in Latin America.  ...  In addition, José Antonio García-Díaz has been supported by Banco Santander and University of Murcia through the Doctorado industrial programme.  ... 
doi:10.1016/j.future.2020.06.019 pmid:32572291 pmcid:PMC7301140 fatcat:xxt6mfojevf3zhpu4fchr3lzuq

Comparative Analysis of Deep Learning Techniques to detect Online Public Shaming

Mehdi Surani, Ramchandra Mangrulkar, V.A. Vyawahare, M.D. Patil
2021 ITM Web of Conferences  
This has led to numerous threats allowing users to exploit their freedom of speech, thus spreading hateful comments, using abusive language, carrying out personal attacks, and sometimes even to the extent  ...  In this research paper, various shaming types, namely toxic, severe toxic, obscene, threat, insult, identity hate, and sarcasm are predicted using deep learning approaches like CNN and LSTM.  ...  [8] model aim to apply the text-based Convolution Neural Network (CNN) with word embedding, using fastText word embedding technique improving the detection of different types of toxicity to improve  ... 
doi:10.1051/itmconf/20214003030 fatcat:dguymrkme5fkhpudh67buqrxye

Did You Really Just Have a Heart Attack? Towards Robust Detection of Personal Health Mentions in Social Media [article]

Payam Karisani, Eugene Agichtein
2018 arXiv   pre-print
Our experiments show that WESPAD outperforms the baselines and state-of-the-art methods, especially in cases when the number and proportion of true health mentions in the training data is small.  ...  Millions of users share their experiences on social media sites, such as Twitter, which in turn generate valuable data for public health monitoring, digital epidemiology, and other analyses of population  ...  In the first step the model uses a long short-term memory neural network (LSTM) to produce the sentence representations, and in the second step, uses a gated recurrent neural network (GRNN) to encode the  ... 
arXiv:1802.09130v2 fatcat:jznfcqz5fjde3l7ydkkg76cpay

A Novel Hybrid Deep Learning Model for Detecting COVID-19-Related Rumors on Social Media Based on LSTM and Concatenated Parallel CNNs

Mohammed Al-Sarem, Abdullah Alsaeedi, Faisal Saeed, Wadii Boulila, Omair AmeerBakhsh
2021 Applied Sciences  
The proposed model is based on a Long Short-Term Memory (LSTM) and Concatenated Parallel Convolutional Neural Networks (PCNN).  ...  The experiments were conducted on an ArCOV-19 dataset that included 3157 tweets; 1480 of them were rumors (46.87%) and 1677 tweets were non-rumors (53.12%).  ...  Acknowledgments: The authors would like to thank the Deanship of Scientific Research at Taibah University, Saudi Arabia, for funding this research project number (CSE-4).  ... 
doi:10.3390/app11177940 fatcat:ey2zek7mazhapp5kmwjggqve2q

Sentiment Analysis Based on Deep Learning: A Comparative Study

Nhan Cach Dang, María N. Moreno-García, Fernando De la Prieta
2020 Electronics  
Models using term frequency-inverse document frequency (TF-IDF) and word embedding have been applied to a series of datasets.  ...  The analysis of sentiment on social networks, such as Twitter or Facebook, has become a powerful means of learning about the users' opinions and has a wide range of applications.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/electronics9030483 fatcat:jbqyaparjbgollkgr5cr6jpaym

Extracting psychiatric stressors for suicide from social media using deep learning

Jingcheng Du, Yaoyun Zhang, Jianhong Luo, Yuxi Jia, Qiang Wei, Cui Tao, Hua Xu
2018 BMC Medical Informatics and Decision Making  
CNN is leading the performance at identifying suicide-related tweets with a precision of 78% and an F-1 measure of 83%, outperforming Support Vector Machine (SVM), Extra Trees (ET), etc.  ...  Specifically, a convolutional neural networks (CNN) based algorithm was used to build the binary classifier. Next, psychiatric stressors were annotated in the suicide-related tweets.  ...  Acknowledgments We thank the anonymous reviewers for their careful reading of our manuscript and their many insightful comments.  ... 
doi:10.1186/s12911-018-0632-8 pmid:30066665 pmcid:PMC6069295 fatcat:qe25rcmdz5hv7kszzkpebvxudu

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

Purva Singh
2020 International Journal on Natural Language Computing  
On 11th March 2020, the World Health Organization (WHO) declared Corona Virus Disease of 2019 (COVID-19) as a pandemic.  ...  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  ...  The proposed framework first experiments with eight different versions of deep learning models: vanilla LSTM + 3 pre-trained word embeddings and vanilla Bi-LSTM + 3 pre-trained word embeddings.  ... 
doi:10.5121/ijnlc.2020.9502 fatcat:5oua23zvrzf6vhughnrwtdu5za

Stance Detection in Web and Social Media: A Comparative Study [chapter]

Shalmoli Ghosh, Prajwal Singhania, Siddharth Singh, Koustav Rudra, Saptarshi Ghosh
2019 Lecture Notes in Computer Science  
Through experiments on two datasets -- (i)~the popular SemEval microblog dataset, and (ii)~a set of health-related online news articles -- we also perform a detailed comparative analysis of various methods  ...  In this work, we explore the reproducibility of several existing stance detection models, including both neural models and classical classifier-based models.  ...  Acknowledgement: The work is partially supported by a project titled "Building Healthcare Informatics Systems Utilising Web Data" funded by Department of Science & Technology, Government of India.  ... 
doi:10.1007/978-3-030-28577-7_4 fatcat:umeexnxhtzbo5eofm7o6azzdfq

How Successful Is Transfer Learning for Detecting Anorexia on Social Media?

Pilar López-Úbeda, Flor Miriam Plaza-del-Arco, Manuel Carlos Díaz-Galiano, Maria-Teresa Martín-Valdivia
2021 Applied Sciences  
The main contribution of this paper is the application of transfer learning techniques using Transformer-based models for detecting anorexia in tweets written in Spanish.  ...  Currently, there is still a long way to go in the identification of anorexia on social media due to the low number of texts available and in fact, most of these are focused on the treatment of English  ...  Figures 1-3 show the architectures followed for the LSTM, BiLSTM and CNN models, respectively. For all previous neural networks, we used an embedding layer as input.  ... 
doi:10.3390/app11041838 fatcat:fddadtr6t5ap7eucebpf3xmduq

A Multi-input Multi-output Transformer-based Hybrid Neural Network for Multi-class Privacy Disclosure Detection [article]

A K M Nuhil Mehdy, Hoda Mehrpouyan
2021 arXiv   pre-print
The results show that the proposed model was able to identify privacy disclosure through tweets with an accuracy of 77.4% while classifying the information type of those tweets with an impressive accuracy  ...  In this paper, we propose a multi-input, multi-output hybrid neural network which utilizes transfer-learning, linguistics, and metadata to learn the hidden patterns.  ...  (CRII) grant number 1657774 of the Secure and Trustworthy  ... 
arXiv:2108.08483v2 fatcat:rrmhgzddurerhoreoxxp7oni7y
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