AUTHOR=Tsapatsoulis Nicolas , Djouvas Constantinos 

TITLE=Opinion Mining From Social Media Short Texts: Does Collective Intelligence Beat Deep Learning?

JOURNAL=Frontiers in Robotics and AI

VOLUME=Volume 5 - 2018

YEAR=2019

URL=https://www.frontiersin.org/journals/robotics-and-ai/articles/10.3389/frobt.2018.00138

DOI=10.3389/frobt.2018.00138

ISSN=2296-9144

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 of 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, basically, emphasise on topic modelling and extract low level features using deep learning approaches. So far, researchers have focused their efforts on opinion mining and especially sentiment analysis of tweets. This trend reflects the availability of Twitter API that simplifies automatic data (tweet) collection and testing of the proposed algorithms in real situations. However, if we really interested in realistic opinion mining we should consider mining opinions form 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 kind 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 area 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 concluded that, even in the era of big data letting people to annotate (a small portion of) data would allow effective artificial intelligence tools to be developed using the learning by example paradigm.