A Societal Sentiment Analysis: Predicting the Values and Ethics of Individuals by Analysing Social Media Content

Tushar Maheshwari, Aishwarya N. Reganti, Samiksha Gupta, Anupam Jamatia, Upendra Kumar, Björn Gambäck, Amitava Das

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.
Anthology ID:
E17-1069
Volume:
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
Editors:
Mirella Lapata, Phil Blunsom, Alexander Koller
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
731–741
Language:
URL:
https://aclanthology.org/E17-1069
DOI:
Bibkey:
Cite (ACL):
Tushar Maheshwari, Aishwarya N. Reganti, Samiksha Gupta, Anupam Jamatia, Upendra Kumar, Björn Gambäck, and Amitava Das. 2017. A Societal Sentiment Analysis: Predicting the Values and Ethics of Individuals by Analysing Social Media Content. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 731–741, Valencia, Spain. Association for Computational Linguistics.
Cite (Informal):
A Societal Sentiment Analysis: Predicting the Values and Ethics of Individuals by Analysing Social Media Content (Maheshwari et al., EACL 2017)
Copy Citation:
PDF:
https://preview.aclanthology.org/naacl24-info/E17-1069.pdf
PDF Search