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Found 6 papers, 2 papers with code
Practitioner-Centric Approach for Early Incident Detection Using Crowdsourced Data for Emergency Services
First, we show how crowdsourced reports, ground-truth historical data, and other relevant determinants such as traffic and weather can be used together in a Convolutional Neural Network (CNN) architecture for early detection of emergency incidents.
Exploring Deep Neural Networks and Transfer Learning for Analyzing Emotions in Tweets
In this paper, we present an experiment on using deep learning and transfer learning techniques for emotion analysis in tweets and suggest a method to interpret our deep learning models.
Emergency Incident Detection from Crowdsourced Waze Data using Bayesian Information Fusion
Crowdsourcing platforms such as Waze provides an opportunity to develop a rapid, `proactive' approach to collect data about incidents through crowd-generated observational reports.
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Traffic Accident Detection
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Evaluating Semantic Feature Representations to Efficiently Detect Hate Intent on Social Media
We hypothesize that semantic features can help enrich the context representation of word senses in a social media post for machine learning algorithms.
DataSEARCH at IEST 2018: Multiple Word Embedding based Models for Implicit Emotion Classification of Tweets with Deep Learning
This paper describes an approach to solve implicit emotion classification with the use of pre-trained word embedding models to train multiple neural networks.
