Using Twitter sentiment and emotions analysis of Google Trends for decisions making. Issue 3 (5th September 2017)

• Record Type: Journal Article
• Authors: D'Avanzo, Ernesto; Pilato, Giovanni; Lytras, Miltiadis
• Is Part Of: Program. Volume 51:Issue 3(2017)
• Page Start: 322
• Page End: 350
• Publication Date: 2017-09-05
• Subjects: Bayesian analysis -- Emotion analysis -- Social behaviour forecasting -- Social sensing -- Social sentiment analysis -- Social trends; Libraries, University and college -- Great Britain -- Automation -- Periodicals; 025.30285
• DOI: 10.1108/PROG-02-2016-0015
• Languages: English
• ISSNs: 0033-0337

Abstract

Abstract : Purpose: An ever-growing body of knowledge demonstrates the correlation among real-world phenomena and search query data issued on Google, as showed in the literature survey introduced in the following. The purpose of this paper is to introduce a pipeline, implemented as a web service, which, starting with recent Google Trends, allows a decision maker to monitor Twitter's sentiment regarding these trends, enabling users to choose geographic areas for their monitors. In addition to the positive/negative sentiments about Google Trends, the pipeline offers the ability to view, on the same dashboard, the emotions that Google Trends triggers in the Twitter population. Such a set of tools, allows, as a whole, monitoring real-time on Twitter the feelings about Google Trends that would otherwise only fall into search statistics, even if useful. As a whole, the pipeline has no claim of prediction over the trends it tracks. Instead, it aims to provide a user with guidance about Google Trends, which, as the scientific literature demonstrates, is related to many real-world phenomena (e.g. epidemiology, economy, political science). Design/methodology/approach: The proposed experimental framework allows the integration of Google search query data and Twitter social data. As new trends emerge in Google searches, the pipeline interrogates Twitter to track, also geographically, the feelings and emotions of Twitter users about new trends. The core of the pipeline is represented by a sentiment analysis framework that make use of a Bayesian machine learning device exploiting deep natural language processing modules to assign emotions and sentiment orientations to a collection of tweets geolocalized on the microblogging platform. The pipeline is accessible as a web service for any user authorized with credentials. Findings: The employment of the pipeline for three different monitoring task (i.e. consumer electronics, healthcare, and politics) shows the plausibility of the proposed approach in order to measure social media sentiments and emotions concerning the trends emerged on Google searches. Originality/value: The proposed approach aims to bridge the gap among Google search query data and sentiments that emerge on Twitter about these trends.