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Processing Affect in Social Media: A Comparison of Methods to Distinguish Emotions in Tweets

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Published:04 January 2017Publication History
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Abstract

Emotion analysis in social media is challenging. While most studies focus on positive and negative sentiments, the differentiation between emotions is more difficult. We investigate the problem as a collection of binary classification tasks on the basis of four opposing emotion pairs provided by Plutchik. We processed the content of messages by three alternative methods: structural and lexical features, latent factors, and natural language processing. The final prediction is suggested by classifiers deriving from the state of the art in machine learning. Results are convincing in the possibility to distinguish the emotions pairs in social media.

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  1. Processing Affect in Social Media: A Comparison of Methods to Distinguish Emotions in Tweets

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          cover image ACM Transactions on Internet Technology
          ACM Transactions on Internet Technology  Volume 17, Issue 1
          Special Issue on Affect and Interaction in Agent-based Systems and Social Media and Regular Paper
          February 2017
          213 pages
          ISSN:1533-5399
          EISSN:1557-6051
          DOI:10.1145/3036639
          • Editor:
          • Munindar P. Singh
          Issue’s Table of Contents

          Copyright © 2017 ACM

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          New York, NY, United States

          Publication History

          • Published: 4 January 2017
          • Accepted: 1 September 2016
          • Revised: 1 August 2016
          • Received: 1 December 2015
          Published in toit Volume 17, Issue 1

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