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2017 EACL EACL 2017

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

Abstract

AbstractTo 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.

🌉 Interdisciplinary Bridge — Interdisciplinary and Machine Learning and Natural Language Processing
🧭 Keyword Pioneer — ethical classification
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

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

Topics

Interdisciplinary > Social > Affective Computing Interdisciplinary > Social > Social Media Analysis Machine Learning > Learning Types > Supervised Learning Natural Language Processing > Applications > Sentiment Analysis

Keywords

sentiment analysis text classification social media analysis support vector machine ethical classification psycholinguistic model schwartz model ethics classification
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