Deep Representation Learning For Clustering Of Health Tweets

Below is result for Deep Representation Learning For Clustering Of Health Tweets in PDF format. You can download or read online all document for free, but please respect copyrighted ebooks. This site does not host PDF files, all document are the property of their respective owners.

2018 AAAI Joint Workshop on Health Intelligence (W3PHIAI 2018)

Session 1: Population Health I (Session Chair: Arash Shaban-Nejad) 1. Using digital purchasing data to generate public health evidence: Learning unhealthy beverage demand from grocery transaction data Hiroshi Mamiya, Xing Han, Lu Xing, Yu Ma, and David L. Buckeridge 2. Detecting Personal Experience Tweets for Health Surveillance Using Word

arXiv:1904.08926v1 [cs.SI] 15 Apr 2019

Apr 22, 2019 of tweets from Bogot a with words related with health symptoms [23]. A third study examined the results of 2015 Colombian regional elections and compared them with political ideology and Twitter activity of the candidates [24]. In the end, it is clear that an unsupervised model for text representation is

EMPIRICAL STUDY OF FEATURES AND UNSUPERVISED SENTIMENT

microblogs, such as tweets, as well as macroblogs, such as posts on Reddit. The study's investigation will concentrate on the linguistic characteristics, blogging behavior, and topics for features, multi-word, and word embeddings for document representation as well as on unsupervised learning for text clustering.

Exploring Pattern Mining Algorithms for Hashtag Retrieval Problem

learning framework to discover the relevant hashtags in the health domain. It uses a deep learning approach to classify the tweets by the distribution representations of the words, which aims at optimizing an objective function for the like-lihood of word occurrences. It ˝rst performs pre-processing tocleantweetsbyremovingURLs(UniformResourceLoca-

Deep neural network and model-based clustering technique for

tweets, but as the number of author s increases, there was a gradual drop in the accuracy measure, with 67% for 20 authors. The analysis conducted by Naser Eddine Benzebouchi et al.[4] stressed the work of the email author identication system using a text representation vector as word2vec. The result was performed as a two-stage process. The rst

Cyberthreat Detection from Twitter using Deep Neural Networks

hate speech in tweets. The authors report that deep learning techniques perform significantly better than other methods. Regarding applications of deep learning for NER tasks using Twitter data, Jimeno-Yepes et al. [12] implemented a sequence-to-sequence LSTM architecture for the annotation of medical entities to support public health

PART OF BIG DATA WEEK OF M.SC. IN BIG DATA

MODELS AND ALGORITHMS FOR DEEP CLUSTERING Deep Embedded Clustering surpasses traditional clustering algorithms by jointly performing feature learning and clustering assignment. Embedded data representation and dimensionality reduction have been used alongside in order to project the input data into a lower dimensional

Capsule Network for Cyberthreat Detection

tweets into security-related and not security-related can help with early warnings for such attacks. In this study, the use of a capsule network (CapsNet), the new deep learning algo-rithm, is investigated for the first time in the field of security attack detection using Twitter. The aim was to increase the

Advances in information retrieval - GBV

Deep Learning I Seed-Guided Deep Document Clustering Part 1 17 33 50 65 83 97 111 126 141 Mazar Moradi Fard, Thibaut Thonet, and Eric Gaussier Improving Knowledge Graph Embedding Using Locally and Globally Attentive Relation Paths Ningning Jia, Xiang Cheng, and Sen Su ReadNet: A Hierarchical Transformer Framework for Web Article

NIH.AI WORKSHOP ON BIOMEDICAL NLP

1:00-1:40pm Text Mining and Deep Learning for Biology and Healthcare: an Introduction Lana Yeganova and Qingyu Chen, NCBI/NLM 1:40-2:15pm Automatic information extraction from free-text pathology reports using multi-task convolutional neural networks Hong-Jun Yoon, Oak Ridge national laboratory 2:15-2:30pm Break

Cs229 Final Report Machine Learning

President Trump's Tweets 2017 Deep learning neural networks have become easy to define and fit, but are still hard to configure. Discover exactly how to improve the performance of deep learning neural network models on your predictive modeling projects. With clear explanations, standard Python libraries, and step-by-step Page 1/23

FROM SUPER COMPUTING TO INTELLIGENT TRANSFORMATIONS

Deep Learning Real world information (images, videos, language, sentiment, sound) has a hierarchical structure Deep networks take advantage of unlabeled data by learning good representations of the data through unsupervised learning CONTRIBUTING FACTORS: Faster Machines, More Data, New Methods of Unsupervised Learning

Large-Scale, Language-Agnostic Discourse Classification of

Nov 29, 2020 as different vectors if it appears in different contexts. Several studies involving tweets utilized these deep neural network techniques or their variants either as a pre-training for further downstream tasks (e.g., classification, clustering, entity recognition) or for learning tweet representations from scratch [60 67].

'Is depression related to cannabis?': A Knowledge-infused

Mental Health, Depression, Cannabis Crisis, Legalization, knowledge infusion, Relation Extraction. 1. Introduction. Many states in the US have legalized the medical use of cannabis for therapeutic relief in those af-fected by Mental Illness [1] [2,3]. The use of cannabis for depression, however, is not authorized yet [4].

Impact of Unreliable Content on Social Media Users during

that shares the facts regarding the claim). Afterward, a deep analysis was conducted on the corpus to gain a thorough understanding of the attitude of different user groups regarding the misinformation. The article also presents the hashtags-based clustering of tweets for detecting echo chambers in the network.

Service quality monitoring in confined spaces through mining

of two consecutive tasks: First, topic modeling, clustering or classification approaches are leveraged for grouping semantically-related tweets [20]. This paper calls this task aspect extraction. Second, machine-learning approaches are employed to detect uncommon high burstiness scores in the study period.

Classification of eyewitness tweets in emergency situations

representation and counting representation. Each example is a paddledarrayofinteger embeddings, pre-trained or trained in place. Type of words Examples Pronouns I, me, ours Senses see, feel, hear Demonstratives this Time-specific words tomorrow, tonight, night, day Location-specific words here, there, north, east Actions

Deep Representation Learning for Clustering of Health Tweets

Deep Representation Learning for Clustering of Health Tweets Oguzhan Gencoglu Abstract Twitter has been a prominent social media platform for mining population-level health data and accurate clustering of health-related tweets into topics is important for extracting relevant health insights. In this work, we propose deep convo-

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 1 Deep

tweets based on length and content. It then applies hierar-175 chical clustering on the refined set of tweets and finally 176 prunes results by weighting. PeakLabel [3] uses a spike 177 TABLE 1 2x2 Contingency Table Outcome of Interest Other Outcomes Entity of Interest a b Other Entities c d a, b, c, and d represent the frequency of occurrence.

Using Machine Learning and Deep Learning Methods to Find

niques. Social Media Mining for Health Ap-plications (SMM4H) provides tasks such as those described in this document to help man-age information in the health domain. This document shows the first participation of the SINAI group in SMM4H. We study ap-proaches based on machine learning and deep learning to extract adverse drug reaction men-

Performance Analysis of Different Word Embedding Models for

lumped into the field of deep learning [13] 1) Word2Vec: Word2Vec is a particularly computationally-efficient predictive model for learning word embeddings from raw text. It consists of a shallow, two-layer neural networks which is trained to reconstruct linguistic contexts of words. It takes as

2nd International Conference on Data Science and - ICDSA 2021

Deep Learning Algorithm for Identification of Ear Disease 37 Extrapolating Z-axis data for a 2D Image on a Single Board Computer 38 Impact Evaluation of Deep Learning Models in the context of Plant Disease Detection

Build Recommender Systems, Detect Network Intrusion

How can Deep Learning be used together with Graph technologies K Means Clustering 640 161s / 256 268s / 144 Comments & tweets

1 Is Depression Related to Cannabis?1 : A Knowledge-Infused

We propose a knowledge-infused deep learning framework based on GPT-3 and domain-specific DAO ontology to extract entities and their relationship. Then we further enhance the utilization of limited supervision through the use of supervised contrastive learning. It is well known that deep understanding requires many examples to generalize.

Python Natural Language Processing Advanced Machine Learning

Discover the concepts of deep learning used for natural language processing (NLP), with full-fledged examples of neural network models such as recurrent neural networks, long short-term memory networks, and sequence-2-sequence models. You ll start by covering the mathematical prerequisites and the fundamentals of deep learning and NLP with

Introduction to Text Mining - University of North Carolina at

May 01, 2018 Representation: a set of features that we believe are useful in recognizing the desired concept Learning algorithm: a computer program that uses the training data to learn a predictive model of the concept Predictive Analysis: basic ingredients Highly Influential

Using Word Embeddings to Explore the Language of Depression

comprised of 178207 tweets from 139 depressed individuals and 192809 tweets from 162 non-depressed individuals. Moreover, average number of tweets per individual

Deep Neural Models for Medical Concept Normalization in User

coming from health care professionals or from the general public in the form of social media texts. We approach it as a sequence learning problem with powerful neural networks such as recurrent neural networks and contextual-ized word representation models trained to ob-tain semantic representations of social media expressions.

AAPOR Webinar - Data Science - Link -11Oct17 - FINAL recvd 10.08

Deep Learning: A class of Machine Learning algorithms that use a cascade of many layers of nonlinear processing units for feature extraction and transformation. Cognitive Computing: The simulation of human thought processes in a computerized model involves self-learning systems that use data mining,pattern recognition, and

Teaching Social Media Analytics in R

Teaching Social Media Analytics in R Design Background Background In 2014, I started developing a new course, social media analytics, in an ffrt to help Simon students understand and

Taking innovative machine learning research to industry

vector using three sentence representation techniques. 1. A psycholinguistic representation that employs features delineated in LIWC. 2. A deep emotion based sentence representation which is captured using DeepMoji. 3. A word embedding based technique that uses a RNN to capture and encode relevant semantic and topical aspects of depression

2 International Conference on Data Science, Machine Learning

Application of Machine Learning to Analyse Handwriting Features of Neuroticism 21 540 Doodle Recognition using Ensemble Learning 22 541 Training optimization for a hardware aware approach to deep learning 23 542 An end-to-end, interactive Deep Learning based Annotation system for cursive and print English handwritten text 24 543

POSTER SESSIONS - JUNE 6 - MCS EVENTS

distributed edge with machine learning application Suyash Nigam (CMU) - PointVox: A deep learning framework to convert point-cloud representation to voxelized representation Arun Rajagopalan (Illinois Inst. Tech) - On the Improved Estimation of Living Space Occupancy using Human Poses Inferred with Computer Vision and Deep Learning to

Automated Detection of Adverse Drug Reactions From Social

learning classi er with a set of features for resolving this problem. Our feature-rich classi er achieves signi cant improvements on a benchmark dataset over baseline approaches and convolutional neural networks. Keywords: adverse drug reactions, text mining, health social media analytics, machine learning, deep learning 1 Introduction

Deep learning: emerging trends, applications and research

various deep learning approaches and architectures for Arabic tweets. Gao and Zhou (2019) presented the privacy protection algorithm and also integrated with deep learning approach to construct the precise poverty alleviation plat-form. Huang et al. (2019a, b, c) integrated the sequential pattern mining (SPM) and association discovery (AD) to

Volume 02, Issue 10, October, 2020 Empirical Study of

media microblogs, such as tweets, as well as macroblogs, such as posts on Reddit. The study's investigation will concentrate on the linguistic characteristics, blogging behavior, and topics for features, multi-word, and word embeddings for document representation as well as on unsupervised learning for text clustering. This

User Profiling and Recommendation Systems

Jianxun Lian, etc. Towards Better Representation Learning for Personalized News Recommendation: a Multi-Channel Deep Fusion Approach, IJCAI 2018 Haochao Ying, Fuzhen Zhuang, Fuzheng Zhang, etc. Sequential Recommender System based on Hierarchical Attention Networks, IJCAI 2018

2nd International Conference on Image Processing and Capsule

learning techniques and deep learning ishan rao , prathmesh shirgire , sanket sanganwar , kedar vyawhare ,prof.s.r.vispute 12.15 to 12.30 pm a diagnostic classifier for prediction of vitamin and mineral deficiency based on symptoms and profiling its impact during pregnancy sawant rupali ,dr. bakal jagdish