As part of the “text-as-data” movement, Natural Language Processing (NLP) provides a
computational way to examine political polarization. We conducted a methodological …

Typically performed by supervised machine learning algorithms, sentiment analysis is highly
useful for extracting subjective information from text documents online. Most approaches that …

Purpose Rapid developments in social networks and their usage in everyday life have
caused an explosion in the amount of short electronic documents. Thus, the need to classify …

The rise of social media has transformed the way the public engages with science
organisations and scientists.'Retweet','Like','Share'and 'Comment'are a few ways users …

This article tackles the problem of performing multilingual polarity classification on Twitter,
comparing three techniques:(1) a multilingual model trained on a multilingual dataset …

With the rise of microblog services in recent years, microblog sentiment classification has
been widely studied and applied in many fields like public opinion monitoring, commodity …

Abstract Current Online Social Networks represent a means for the continuous generation
and distribution of information, which is slightly changed when moving from a user to …

Over the past five years, topic models have been applied to bibliometrics research as an
efficient tool for discovering latent and potentially useful content. The combination of topic …

Over the past several years, social networks have become a major channel for information
delivery. At present, social networks are being used to obtain more followers and exert …

Twitter goes to the Doctor: Detecting Medical Tweets using Machine Learning and BERT
Page 1 Twitter goes to the Doctor: Detecting Medical Tweets using Machine Learning and …