Ekman's Basic Emotions Research Articles

A Lexicon Based Term Weighting Scheme for Emotion Identification of Tweets

Detecting emotions in tweets is a huge challenge due to its limited 140 characters and extensive use of twitter language with evolving terms and slangs. This paper uses various preprocessing techniques, forms a feature vector using lexicons and classifies tweets into Paul Ekman's basic emotions namely, happy, sad, anger, fear, disgust and surprise using machine learning. Preprocessing is done using the dictionaries available for emoticons, interjections and slangs and by handling punctuation marks and hashtags. The feature vector is created by combining words from the NRC Emotion lexicon, WordNet-Affect and online thesaurus. Feature vectors are assigned weight based on the presence of punctuations and negations in the feature and the tweets are classified using naive Bayes, SVM and random forests. The use of lexicon features and a novel weighting scheme has produced a considerable gain in terms of accuracy with random forest achieving maximum accuracy of almost 73%.

A lexicon-based term weighting scheme for emotion identification of tweets

Detecting emotions in tweets is a huge challenge due to its limited 140 characters and extensive use of twitter language with evolving terms and slangs. This paper uses various preprocessing techniques, forms a feature vector using lexicons and classifies tweets into Paul Ekman's basic emotions namely, happy, sad, anger, fear, disgust and surprise using machine learning. Preprocessing is done using the dictionaries available for emoticons, interjections and slangs and by handling punctuation marks and hashtags. The feature vector is created by combining words from the NRC Emotion lexicon, WordNet-Affect and online thesaurus. Feature vectors are assigned weight based on the presence of punctuations and negations in the feature and the tweets are classified using naive Bayes, SVM and random forests. The use of lexicon features and a novel weighting scheme has produced a considerable gain in terms of accuracy with random forest achieving maximum accuracy of almost 73%.

Alexithymia and impaired facial affect recognition in multiple sclerosis

Despite the high relevance of emotion processing for social functioning, the study of the impairment of facial affect in multiple sclerosis (MS) has received little attention. Previous research reported evidence for emotion processing deficits but the nature and extent are not fully explained. Thirty-five MS patients underwent dedicated neuropsychological assessment of emotion processing using two facial affect recognition tasks and self-report measures of alexithymia. For comparison, healthy participants served as controls. Relative to healthy controls, MS patients were impaired in facial affect recognition on four of the six Ekman basic emotions, except happiness and disgust. The MS patients were more alexithymic than the healthy controls. These data provide evidence for deficits in the recognition of emotional face expressions and emotional introspection.

Ekman's basic emotions: Why not love and jealousy?

Paul Ekman's view of the emotions is, we argue, pervasive in psychology and is explicitly shaped to be compatible with evolutionary thinking. Yet, strangely, jealousy and parental love, two emotions that figure prominently in evolutionary psychology, are absent from Ekman's list of the emotions. In this paper we examine why Ekman believes this exclusion is necessary, and what this implies about the limits of his conception of emotion. We propose an alternative way of thinking about emotion that does not exclude jealousy and parental love.