Sentiment analysis has proven to be a valuable tool to gauge public opinion in different
disciplines. It has been successfully employed in financial market prediction, health issues …

Sentiment analysis is a critical subfield of natural language processing that focuses on
categorizing text into three primary sentiments: positive, negative, and neutral. With the …

The social and behavioral sciences have been increasingly using automated text analysis to
measure psychological constructs in text. We explore whether GPT, the large-language …

Sentiment analysis (SA) has been a long-standing research area in natural language
processing. It can offer rich insights into human sentiments and opinions and has thus seen …

The experimental landscape in natural language processing for social media is too
fragmented. Each year, new shared tasks and datasets are proposed, ranging from classics …

Despite its importance, the time variable has been largely neglected in the NLP and
language model literature. In this paper, we present TimeLMs, a set of language models …

Pre-trained language models (LMs) are currently integral to many natural language
processing systems. Although multilingual LMs were also introduced to serve many …

Deep learning has unlocked new paths towards the emulation of the peculiarly-human
capability of learning from examples. While this kind of bottom-up learning works well for …

Sentiment is central to many studies of communication science, from negativity and
polarization in political communication to analyzing product reviews and social media …

We present BERTweet, the first public large-scale pre-trained language model for English
Tweets. Our BERTweet, having the same architecture as BERT-base (Devlin et al., 2019), is …