@InProceedings{maheshwari-EtAl:2017:EACLlong,
  author    = {Maheshwari, Tushar  and  Reganti, Aishwarya N.  and  Gupta, Samiksha  and  Jamatia, Anupam  and  Kumar, Upendra  and  Gamb\"{a}ck, Bj\"{o}rn  and  Das, Amitava},
  title     = {A Societal Sentiment Analysis: Predicting the Values and Ethics of Individuals by Analysing Social Media Content},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {731--741},
  abstract  = {To find out how users' social media behaviour and language are related to their
	ethical practices, the paper investigates applying Schwartz' psycholinguistic
	model of societal sentiment to social media text. The analysis is based on
	corpora collected from user essays as well as social media (Facebook and
	Twitter). Several experiments were carried out on the corpora to classify the
	ethical values of users, incorporating Linguistic Inquiry Word Count analysis,
	n-grams, topic models, psycholinguistic lexica, speech-acts, and non-linguistic
	information, while applying a range of machine learners (Support Vector
	Machines, Logistic Regression, and Random Forests) to identify the best
	linguistic and non-linguistic features for automatic classification of values
	and ethics.},
  url       = {http://www.aclweb.org/anthology/E17-1069}
}