Detecting variation of emotions in online activities

Highlights

• Detect wide spectrum of emotions from online text sources.

• Reveal social emotions and affective states from online social networks content.

• A case study where explicit and implicit emotion experiences are monitored.

• Online text sources features for emotion detection.

Abstract

Online text sources form evolving large scale data repositories out of which valuable knowledge about human emotions can be derived. Beyond the primary emotions which refer to the global emotional signals, deeper understanding of a wider spectrum of emotions is important to detect online public views and attitudes. The present work is motivated by the need to test and provide a system that categorizes emotion in online activities. Such a system can be beneficial for online services, companies recommendations, and social support communities. The main contributions of this work are to: (a) detect primary emotions, social ones, and those that characterize general affective states from online text sources, (b) compare and validate different emotional analysis processes to highlight those that are most efficient, and (c) provide a proof of concept case study to monitor and validate online activity, both explicitly and implicitly. The proposed approaches are tested on three datasets collected from different sources, i.e., news agencies, Twitter, and Facebook, and on different languages, i.e., English and Greek. Study results demonstrate that the methodologies at hand succeed to detect a wider spectrum of emotions out of text sources.

Introduction

Web 2.0 technologies are increasingly dominating peoples’ everyday life, such that constant and evolving digital social interactions are produced dynamically. Their impact in society is evident by the exponential rates of users and their interactions carried out in popular “mega” social networks platforms, e.g., the daily active users of Facebook overcome 1 billion (Sept. 2016).¹ Such intense and large scale online presence is characterized by many behavioral norms driven by people’s emotions and views. The power of emotion is evident from recent work which documents that contagious effects in online social networks (OSNs) are due to users emotional states which are often transmitted from real to online life (Coviello et al., 2014).

Until recently, emphasis has been placed on capturing human sentiments by detecting positive and negative opinions on various text sources. However, since human emotions are more variable, not necessarily restricted to a dual emotional standing, a more challenging endeavor is to track and reveal broader emotions such as anger, joy, etc., since they are powerful elicitors and indicators of human motivational and perceptual states (Roseman, Spindel, & Jose, 1990). Ongoing discussion in the psychological science classifies emotions in terms of at least two categories:

• the basic or primary, i.e., a fixed number of emotions as the ones we experience instantly as a response to a pleasant (or unpleasant) stimulus. A widely recognized approach of Ekman, Friesen, and Ellsworth (1982) identifies six primary emotions, i.e., ‘anger, disgust, fear, joy, sadness, surprise’. Main characteristics of primary emotions are their automatic onset and pervasive impact on individuals’ cognitive and behavioral outcomes.

• the social emotions, where a person’s emotions are influenced by her fellows emotions and impact their emotions too (Parkinson, Fischer, & Manstead, 2005). Indicative social emotions are the ‘rejection’, and ‘shame’ which have been identified as quite important in social interactions (Kafetsios & Nezlek, 2012).

Up to now, efforts in emotion analysis on text sources have mostly focused on detecting emotions of individuals ignoring the social context’s influence and impact. This work is motivated by the need to deepen emotional detection by exploiting not only primary emotions, but also other facets of emotions such as social emotions. A full list of emotions from a psychological perspective is not complete without reference to emotions that are neither basic or social but that characterize general individual affective states, such as feeling anxious, calm, and interest, i.e., emotional states that demonstrate a longer duration and cause less intensive experiences (Ekman, 1992). Existing work mainly targets in detecting primary emotions without considering the socials ones, while only limited work has targeted in detecting those that characterize general affective states.

The present work proposes an extended emotions analysis approach which incorporates Ekman’s primary emotions (enabling comparisons with existing work) together with a wider spectrum of emotions at which social and more general affective states are also considered. We leverage both machine learning and lexicon-based approaches which have dominated the literature on this area so far, under a separate or hybrid scheme. Motivation for building on a hybrid scheme originates from the fact that the existing lexicon-based approaches tend to achieve high precision and low recall (Nie, Wang, He, & Sato, 2015), while machine learning approaches suffer in integrating syntactic with semantic information (El-Alfy, Thampi, Takagi, Piramuthu, & Hannen, 2015). In summary, the main contributions of this work are as follows.

• We proceed with a hybrid approach which builds upon machine learning and lexicon-based approaches, to detect Ekman’s primary emotions. To validate such approach and to compare it to existing work we utilize the SemEval-2007 Affective text competition dataset.²

• We examine distinct approaches to detect social emotions and those that characterize general affective states in addition to the primary ones. We experiment with a Twitter dataset annotated by a crowdsourcing process.

• We implement a case study in which explicit (i.e., human reports) versus implicit (i.e., automatic detection of emotions) emotional experiences are monitored to cross validate results. The participants’ native language is Greek, and so issues related to the Greek texting habits and the detection of emotions in non-English texts in general are also considered.

• We share the annotated Twitter dataset at: http://bit.ly/2bLgVUP.

The remainder of the paper is organized as follows. Section 2 reviews literature on emotional analysis. Section 3 proceeds with the data preparation for analysis, while Section 4 presents the used datasets. Section 5 overviews the used methodologies. Sections 6 and 7 proceed with the emotions detection, i.e., the primary ones or the wider spectrum, accordingly. Section 8 presents the case study, while Section 9 concludes the paper.