Emotion Mining in Social Media Data

Abstract

Emotions are known to influence the perception of human beings along with their memory, thinking and imagination. Human perception is important in today's world in a wide range of factors including but not limited to business, education, art, and music. Microblogging and Social networking sites like Twitter, Facebook are challenging sources of information that allow people to share their feelings and thoughts on a daily basis. In this paper we propose an approach to automatically detect emotions on Twitter messages that explores characteristics of the tweets and the writer's emotion using Support Vector Machine LibLinear model and achieve 98% accuracy. Emotion mining gained attraction in the field of computer science due to the vast variety of systems that can be developed and promising applications like remote health care system, customer care services, smart phones that react based on users's emotion, vehicles that sense emotion of the driver. These emotions help understand the current state of user. In order to perform suitable actions or provide suggestions on how user's can enhance their feeling for a better healthy lifestyle we use actionable recommendations. In this work we extract action rules with respect to the user emotions that help provide suggestions for user's.

Neuromarketing as an Emotional Connection Tool Between Organizations and Audiences in Social Networks. A Theoretical Review

“…These authors presented a framework for understanding the choice of certain words in sports communication, their association with social interests, the complexity of thought, and other psychological processes. Also, Ranganathan and Tzacheva (2019) proposed a model for the automatic detection of emotions in Twitter messages. Considering the emotions of the user, their research allows extracting rules of action to provide suggestions with a wide variety of applications in teaching, customer satisfaction, or business improvement models, following the automatic data classification model Support Vector Machine LibLinear, by Fan et al (2008) .…”

“…Business communication is presented as the main subject of research by Chmiel et al (2011) , de Vries et al (2012) , Teixeira et al (2012) , Cvijikj and Michahelles (2013) , Goh et al (2013) , Hollebeek and Chen (2014) , Hudson et al (2015) , Hudson et al (2016) , Kim et al (2015) , Khan et al (2016) , Marbach et al (2016) , Dessart (2017) , Kim and Yang (2017) , Schultz (2017) , Swani and Milne (2017) , Swani et al (2017) , Xu and Wu (2017) , Rout et al (2018) , Vignal Lambret and Barki (2018) , and Moussa (2019) . Personal communication is analyzed in studies by Mauri et al (2011) , Meshi et al (2013) , Nelson-Field et al (2013) , Brynielsson et al (2014) , Coviello et al (2014) , Tandoc et al (2014) , Carrillo et al (2015) , Ferrara and Yang (2015) , Lin and Utz (2015) , Lee and Hong (2016) , Wang et al (2017) , Fan et al (2018) , Ng and Kozlowski (2018) , Turel et al (2018) , Vermeulen et al (2018) , Barry et al (2019) , Min and Yun (2019) , Nash et al (2019) , Ranganathan and Tzacheva (2019) , and Sion (2019) .…”