Please use this identifier to cite or link to this item:
Title: Opinion mining from social media short texts: Does collective intelligence beat deep learning?
Authors: Tsapatsoulis, Nicolas 
Djouvas, Constantinos 
Keywords: Collective intelligence;Crowdsourcing;Deep learning;Opinion mining;Sentiment analysis;Social media messages
Category: Computer and Information Sciences
Field: Natural Sciences
Issue Date: 22-Jan-2019
Publisher: Frontiers Media S.A.
Source: Frontiers Robotics AI, 2019, Volume 6, Issue JAN, Article number 138
Journal: Frontiers Robotics AI 
Abstract: The era of big data has, among others, three characteristics: the huge amounts of data created every day and in every form by everyday people, artificial intelligence tools to mine information from those data and effective algorithms that allow this data mining in real or close to real time. On the other hand, opinion mining in social media is nowadays an important parameter of social media marketing. Digital media giants such as Google and Facebook developed and employed their own tools for that purpose. These tools are based on publicly available software libraries and tools such as Word2Vec (or Doc2Vec) and fasttext, which emphasize topic modeling and extract low-level features using deep learning approaches. So far, researchers have focused their efforts on opinion mining and especially on sentiment analysis of tweets. This trend reflects the availability of the Twitter API that simplifies automatic data (tweet) collection and testing of the proposed algorithms in real situations. However, if we are really interested in realistic opinion mining we should consider mining opinions from social media platforms such as Facebook and Instagram, which are far more popular among everyday people. The basic purpose of this paper is to compare various kinds of low-level features, including those extracted through deep learning, as in fasttext and Doc2Vec, and keywords suggested by the crowd, called crowd lexicon herein, through a crowdsourcing platform. The application target is sentiment analysis of tweets and Facebook comments on commercial products. We also compare several machine learning methods for the creation of sentiment analysis models and conclude that, even in the era of big data, allowing people to annotate (a small portion of) data would allow effective artificial intelligence tools to be developed using the learning by example paradigm.
ISSN: 22969144
DOI: 10.3389/frobt.2018.00138
Rights: © 2019 Tsapatsoulis and Djouvas.
Type: Article
Appears in Collections:Άρθρα/Articles

Files in This Item:
File Description SizeFormat
frobt-05-00138.pdfFulltext1.1 MBAdobe PDFView/Open
Show full item record


checked on Jan 22, 2020

Page view(s)

Last Week
Last month
checked on Jan 28, 2020


checked on Jan 28, 2020

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.