TY - GEN
T1 - Identifying emotions in social media
T2 - 5th IEEE International Conference on Future Internet of Things and Cloud Workshops, W-FiCloud 2017
AU - Kušen, Ema
AU - Cascavilla, Giuseppe
AU - Figl, Kathrin
AU - Conti, Mauro
AU - Strembeck, Mark
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/11/16
Y1 - 2017/11/16
N2 - In recent years, emotions expressed in social media messages have become a vivid research topic due to their influence on the spread of misinformation and online radicalization over online social networks. Thus, it is important to correctly identify emotions in order to make inferences from social media messages. In this paper, we report on the performance of three publicly available word-emotion lexicons (NRC, DepecheMood, EmoSenticNet) over a set of Facebook and Twitter messages. To this end, we designed and implemented an algorithm that applies natural language processing (NLP) techniques along with a number of heuristics that reflect the way humans naturally assess emotions in written texts. In order to evaluate the appropriateness of the obtained emotion scores, we conducted a questionnaire-based survey with human raters. Our results show that there are noticeable differences between the performance of the lexicons as well as with respect to emotion scores the human raters provided in our survey.
AB - In recent years, emotions expressed in social media messages have become a vivid research topic due to their influence on the spread of misinformation and online radicalization over online social networks. Thus, it is important to correctly identify emotions in order to make inferences from social media messages. In this paper, we report on the performance of three publicly available word-emotion lexicons (NRC, DepecheMood, EmoSenticNet) over a set of Facebook and Twitter messages. To this end, we designed and implemented an algorithm that applies natural language processing (NLP) techniques along with a number of heuristics that reflect the way humans naturally assess emotions in written texts. In order to evaluate the appropriateness of the obtained emotion scores, we conducted a questionnaire-based survey with human raters. Our results show that there are noticeable differences between the performance of the lexicons as well as with respect to emotion scores the human raters provided in our survey.
KW - Emotions
KW - Social network
KW - Word-emotion lexicon
UR - http://www.scopus.com/inward/record.url?scp=85047435044&partnerID=8YFLogxK
U2 - 10.1109/FiCloudW.2017.75
DO - 10.1109/FiCloudW.2017.75
M3 - Conference contribution
AN - SCOPUS:85047435044
T3 - Proceedings - 2017 5th International Conference on Future Internet of Things and Cloud Workshops, W-FiCloud 2017
SP - 132
EP - 137
BT - Proceedings - 2017 5th International Conference on Future Internet of Things and Cloud Workshops, W-FiCloud 2017
A2 - Awan, Irfan
A2 - Portela, Filipe
A2 - Younas, Muhammad
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 21 August 2017 through 23 August 2017
ER -