Social Emotion Mining: An Insight

Emotions are an indispensable component of variety of texts present on online social media services. A lot of research has been done to detect and analyse the emotions present in text but most of them are done from the author’s perspective. This paper focuses on providing an in-depth survey of different work done in Social Emotion Mining (SEM) from reader’s perspective. It is a first attempt towards categorization of existing literature into emotion mining levels. It also highlights different models and techniques utilized by various authors in this area. Major limitations and challenges in this area of Emotion Detection and Analysis are also presented

Recognition of Emotions in Czech Newspaper Headlines

With the growth of internet community, many different text-based documents are produced. Emotion detection and classification in text becomes very important in human-machine interaction or in human-to-human internet communication with this growth. This article refers to this issue in Czech texts. Headlines were extracted from Czech newspapers and Fear, Joy, Anger, Disgust, Sadness, and Surprise emotions are detected. In this work, several algorithms for learning were assessed and compared according to their accuracy of emotion detection and classification of news headlines. The best results were achieved using the SVM (Support Vector Machine) method with a linear kernel, where the presence of the dominant emotion or emotions was analyzed. For individual emotions the following results were obtained: Anger was detected in 87.3 %, Disgust 95.01%, Fear 81.32 %, Joy 71.6 %, Sadness 75.4 %, and Surprise 71.09 %

Predicting News Values from Headline Text and Emotions

We present a preliminary study on predicting news values from headline text and emotions. We perform a multivariate analysis on a dataset manually annotated with news values and emotions, discovering interesting correlations among them. We then train two competitive machine learning models – an SVM and a CNN – to predict news values from headline text and emotions as features. We find that, while both models yield a satisfactory performance, some news values are more difficult to detect than others, while some profit more from including emotion information