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Longitudinal Sentiment Topic Modelling of Reddit Posts
In this study, we analyze texts of Reddit posts written by students of four major Canadian universities.
Longitudinal Sentiment Classification of Reddit Posts
We report results of a longitudinal sentiment classification of Reddit posts written by students of four major Canadian universities.
Sentiment Analysis of Covid-related Reddits
This paper focuses on Sentiment Analysis of Covid-19 related messages from the r/Canada and r/Unitedkingdom subreddits of Reddit.
Sentiment Analysis of the COVID-related r/Depression Posts
In this study, we analyzed sentiments of COVID-related messages posted on r/Depression.
Explainable Multi-class Classification of the CAMH COVID-19 Mental Health Data
To the best of these authors knowledge, our study is the first explainable Machine Learning study of the mental health data collected during Covid-19 pandemics.
Convolutional Neural Networks in Multi-Class Classification of Medical Data
We report applications of Convolutional Neural Networks (CNN) to multi-classification classification of a large medical data set.
Explainable Multi-class Classification of Medical Data
In this paper, we present explainable multi-class classification of a large medical data set.
Machine Learning Evaluation of the Echo-Chamber Effect in Medical Forums
We propose the Echo-Chamber Effect assessment of an online forum.
Thumbs Up and Down: Sentiment Analysis of Medical Online Forums
In the current study, we apply multi-class and multi-label sentence classification to sentiment analysis of online medical forums.
Word2Vec and Doc2Vec in Unsupervised Sentiment Analysis of Clinical Discharge Summaries
We have shown that the Word2vec and Doc2Vec methods complement each other results in sentiment analysis of the data sets.
Corpus Statistics in Text Classification of Online Data
Transformation of Machine Learning (ML) from a boutique science to a generally accepted technology has increased importance of reproduction and transportability of ML studies.
One Single Deep Bidirectional LSTM Network for Word Sense Disambiguation of Text Data
Due to recent technical and scientific advances, we have a wealth of information hidden in unstructured text data such as offline/online narratives, research articles, and clinical reports.
Inter-Annotator Agreement in Sentiment Analysis: Machine Learning Perspective
Manual text annotation is an essential part of Big Text analytics.
Studying Positive Speech on Twitter
In fully automated studies, we tested two approaches: unsupervised statistical analysis, and supervised text classification based on distributed word representation.
Topic Modelling and Event Identification from Twitter Textual Data
The tremendous growth of social media content on the Internet has inspired the development of the text analytics to understand and solve real-life problems.
Multi-Labeled Classification of Demographic Attributes of Patients: a case study of diabetics patients
Automated learning of patients demographics can be seen as multi-label problem where a patient model is based on different race and gender groups.
