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

Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 731–741, Valencia, Spain, April 3-7, 2017. c⃝2017 Association for Computational Linguistics A Societal Sentiment Analysis: Predicting the Values and Ethics of Individuals by Analysing Social Media Content Tushar Maheshwari1∗Aishwarya N Reganti1∗ Samiksha Gupta2 Anupam Jamatia3 Upendra Kumar1 Bj¨orn Gamb¨ack4,5 Amitava Das1 1 Indian Institute of Information Technology, Sri City, Andhra Pradesh, India 2 Indian Institute of Technology, Varanasi, Uttar Pradesh, India 3 National Institute of Technology, Agartala, Tripura, India 4 Norwegian University of Science and Technology, Trondheim, Norway 5 SICS Swedish ICT AB, Kista, Sweden 1 {tushar.m14,aishwarya.r14,upendra.k14,amitava.das}@iiits.in 2 samiksha.gupta.apm13@itbhu.ac.in 3 anupamjamatia@gmail.com 4 gamback@idi.ntnu.no Abstract To find out how users’ social media be- haviour and language are related to their ethical practices, the paper investigates ap- plying 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 experi- ments were carried out on the corpora to classify the ethical values of users, incor- porating Linguistic Inquiry Word Count analysis, n-grams, topic models, psycho- linguistic lexica, speech-acts, and non- linguistic information, while applying a range of machine learners (Support Vector Machines, Logistic Regression, and Ran- dom Forests) to identify the best linguistic and non-linguistic features for automatic classification of values and ethics. 1 Introduction In the recent years, there have been sig- nificant efforts on determining the opin- ion/sentiment/emotion about a specific topic held by the author of a piece of text, and on automatic sentiment strength analysis of text, classifying it into either one of the classes posi- tive, negative or neutral, or into Ekman’s classes of happy, sad, anger, fear, surprise, and disgust. However, the intrinsic value of the lives we lead reflects the strength of our values and ethics which guide our social practices, attitude and be- haviour. This paper reports work on investigating ∗ The two first authors contributed equally. a psycholinguistic model, the Schwartz model (Schwartz and Bilsky, 1990; Schwartz, 2012), and applying it to social media text. It will here be referred to as a societal sentiment model, since societal values grow from the interactions, and the views and sentiment of the society are key to ethical practices. No computational model for Schwartz’ Values has been tested or examined before as such, but there has been a growing interest in the scientific community on doing automatic personality recognition, commonly using the Big 5 factor model (Goldberg, 1990) that defines personality traits such as openness, conscientiousness, extraversion, agreeableness, and neuroticism. The Schwartz values model defines ten dis- tinct ethical values (henceforth only values), that respectively are: Achievement sets goals and achieves them; Benevolence seeks to help others and provide general welfare; Conformity obeys clear rules, laws and structures; Hedonism seeks pleasure and enjoyment; Power controls and dom- inates others, controls resources; Security seeks health and safety; Self-direction wants to be free and independent; Stimulation seeks excitement and thrills; …

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