Automatic emotion detection in text is concerned with using natural language processing
techniques to recognize emotions expressed in written discourse. Endowing computers with …

People express emotions as part of everyday communication. Emotions can be judged by a
combination of cues such as facial expressions, prosodies, gestures, and actions. Emotions …

Detecting emotions on microblogging sites such as Twitter is a subject of interest among
researchers in behavioral studies investigating how people react to different events, topics …

The rise of micro-blogging in recent years has resulted in significant access to emotion-
laden text. Unlike emotion expressed in other textual sources (eg, blogs, quotes in …

Detecting emotions in microblogs and social media posts has applications for industry,
health, and security. Statistical, supervised automatic methods for emotion detection rely on …

Sentiment analysis can go beyond the typical granularity of polarity that assumes each text
to be positive, negative or neural. Indeed, human emotions are much more diverse, and it is …

The automatic detection of emotions in texts and the generation of texts that express
emotions is important for applications such as natural language interfaces, e-learning …

Expanding social networks have led to a collateral growth of user generated content on the
web. Micro-blogs have positioned themselves as a very common and popular channel of …

This paper describes EmoTweet-28, a carefully curated corpus of 15,553 tweets annotated
with 28 emotion categories for the purpose of training and evaluating machine learning …

This paper describes a method to expose a set of categories that are representative of the
emotions expressed on Twitter inductively from data. The method can be used to expand the …