Using Hashtags to Capture Fine Emotion Categories from Tweets

Abstract

Detecting emotions in microblogs and social media posts has applications for industry, health, and security. Statistical, supervised automatic methods for emotion detection rely on text that is labeled for emotions, but such data are rare and available for only a handful of basic emotions. In this article, we show that emotion-word hashtags are good manual labels of emotions in tweets. We also propose a method to generate a large lexicon of word-emotion associations from this emotion-labeled tweet corpus. This is the first lexicon with real-valued word-emotion association scores. We begin with experiments for six basic emotions and show that the hashtag annotations are consistent and match with the annotations of trained judges. We also show how the extracted tweet corpus and word-emotion associations can be used to improve emotion classification accuracy in a different nontweet domain.Eminent psychologist Robert Plutchik had proposed that emotions have a relationship with personality traits. However, empirical experiments to establish this relationship have been stymied by the lack of comprehensive emotion resources. Because personality may be associated with any of the hundreds of emotions and because our hashtag approach scales easily to a large number of emotions, we extend our corpus by collecting tweets with hashtags pertaining to 585 fine emotions. Then, for the first time, we present experiments to show that fine emotion categories such as those of excitement, guilt, yearning, and admiration are useful in automatically detecting personality from text. Stream-of-consciousness essays and collections of Facebook posts marked with personality traits of the author are used as test sets.

GradAscent at EmoInt-2017: Character and Word Level Recurrent Neural Network Models for Tweet Emotion Intensity Detection

“…We always convert a message to lowercase before feeding it to the models. (Saif and Kiritchenko, 2015), and SentiWordNet (Baccianella et al, 2010) with traditional NLP features like word-and character ngrams, POS tags (Gimpel et al, 2011), and processing of negations. In addition to those features, AffectiveTweets incorporates SentiStrength values (Thelwall et al, 2012), Brown clusters (Brown et al, 1992) trained on ∼53 million tweets 2 , combining them with averaged and concatenated first k word embeddings of the tweet.…”

Mining Affective Context in Short Films for Emotion-Aware Recommendation

“…The detection of sentiments in short informal texts is described in [22] and in [27]. In [22] the authors present a system for sentiment detection based on a supervised statistical text classification approach and derive the sentiment features from tweet-specific sentiment lexicons.…”

“…In [22] the authors present a system for sentiment detection based on a supervised statistical text classification approach and derive the sentiment features from tweet-specific sentiment lexicons. The work in [27] shows emotion-word hashtags as good labels for emotions in tweets and proposes the use of emotion-labeled tweets as a method to generate a word-emotion association lexicon.…”

Detection and Visualization of Emotions in an Affect-Aware City

“…To avoid the high effort of manual annotation, [17] and [18] created a large corpus of emotional tweets by specifically searching for Twitter messages containing one of the six emotion words as hashtags. This corpus is used to train and cross-validate support vector machines that classify the six basic emotions.…”