Using Twitter sentiment and emotions analysis of Google Trends for decisions making

2017 ◽  
Vol 51 (3) ◽  
pp. 322-350 ◽  
Author(s):  
Ernesto D’Avanzo ◽  
Giovanni Pilato ◽  
Miltiadis Lytras
Keyword(s):  
Web Service ◽  
Language Processing ◽  
Real World ◽  
Consumer Electronics ◽  
Google Trends ◽  
Search Query ◽  
Content Type ◽  
Monitoring Task ◽  
Twitter Users ◽  
Google Search

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.

Sentiment-based commercial real estate forecasting with Google search volume data

2014 ◽  
Vol 32 (6) ◽  
pp. 540-569 ◽  
Author(s):  
Marian Alexander Dietzel ◽  
Nicole Braun ◽  
Wolfgang Schäfers
Keyword(s):  
Real Estate ◽  
Commercial Real Estate ◽  
Google Trends ◽  
Internet Search ◽  
Price Indices ◽  
Search Query ◽  
Content Type ◽  
Forecasting Models ◽  
Search Data ◽  
Google Search

Purpose – The purpose of this paper is to examine internet search query data provided by “Google Trends”, with respect to its ability to serve as a sentiment indicator and improve commercial real estate forecasting models for transactions and price indices. Design/methodology/approach – This paper examines internet search query data provided by “Google Trends”, with respect to its ability to serve as a sentiment indicator and improve commercial real estate forecasting models for transactions and price indices. Findings – The empirical results show that all models augmented with Google data, combining both macro and search data, significantly outperform baseline models which abandon internet search data. Models based on Google data alone, outperform the baseline models in all cases. The models achieve a reduction over the baseline models of the mean squared forecasting error for transactions and prices of up to 35 and 54 per cent, respectively. Practical implications – The results suggest that Google data can serve as an early market indicator. The findings of this study suggest that the inclusion of Google search data in forecasting models can improve forecast accuracy significantly. This implies that commercial real estate forecasters should consider incorporating this free and timely data set into their market forecasts or when performing plausibility checks for future investment decisions. Originality/value – This is the first paper applying Google search query data to the commercial real estate sector.

scholarly journals RAMP – the Repository Analytics and Metrics Portal

2017 ◽  
Vol 35 (1) ◽  
pp. 144-158 ◽  
Author(s):  
Patrick OBrien ◽  
Kenning Arlitsch ◽  
Jeff Mixter ◽  
Jonathan Wheeler ◽  
Leila Belle Sterman
Keyword(s):  
Web Service ◽  
Design Methodology ◽  
Research Process ◽  
Direct Access ◽  
Test Results ◽  
Proof Of Concept ◽  
Content Type ◽  
Reporting Framework ◽  
Log File ◽  
Google Search

Purpose The purpose of this paper is to present data that begin to detail the deficiencies of log file analytics reporting methods that are commonly built into institutional repository (IR) platforms. The authors propose a new method for collecting and reporting IR item download metrics. This paper introduces a web service prototype that captures activity that current analytics methods are likely to either miss or over-report. Design/methodology/approach Data were extracted from DSpace Solr logs of an IR and were cross-referenced with Google Analytics and Google Search Console data to directly compare Citable Content Downloads recorded by each method. Findings This study provides evidence that log file analytics data appear to grossly over-report due to traffic from robots that are difficult to identify and screen. The study also introduces a proof-of-concept prototype that makes the research method easily accessible to IR managers who seek accurate counts of Citable Content Downloads. Research limitations/implications The method described in this paper does not account for direct access to Citable Content Downloads that originate outside Google Search properties. Originality/value This paper proposes that IR managers adopt a new reporting framework that classifies IR page views and download activity into three categories that communicate metrics about user activity related to the research process. It also proposes that IR managers rely on a hybrid of existing Google Services to improve reporting of Citable Content Downloads and offers a prototype web service where IR managers can test results for their repositories.

scholarly journals Prediction of Zika-confirmed cases in Brazil and Colombia using Google Trends

2018 ◽  
Vol 146 (13) ◽  
pp. 1625-1627 ◽  
Author(s):  
S. Morsy ◽  
T.N. Dang ◽  
M.G. Kamel ◽  
A.H. Zayan ◽  
O.M. Makram ◽  
...  
Keyword(s):  
Zika Virus ◽  
Surveillance System ◽  
Web Search ◽  
Low Cost ◽  
Google Trends ◽  
Search Query ◽  
Time Series Regression ◽  
Search Volume ◽  
Advance Time ◽  
Google Search

AbstractZika virus infection in humans has been linked to severe neurological sequels and foetal malformations. The rapidly evolving epidemics and serious complications made the frequent updates of Zika virus mandatory. Web search query has emerged as a low-cost real-time surveillance system to anticipate infectious diseases’ outbreaks. Hence, we developed a prediction model that could predict Zika-confirmed cases based on Zika search volume in Google Trends. We extracted weekly confirmed Zika cases of two epidemic countries, Brazil and Colombia. We got the weekly Zika search volume in the two countries from Google Trends. We used standard time-series regression (TSR) to predict the weekly confirmed Zika cases based on the Zika search volume (Zika query). The basis TSR model – using 1-week lag of Zika query and using 1-week lag of Zika cases as a control for autocorrelation – was the best for predicting Zika cases in Brazil and Colombia because it balanced the performance of the model and the advance time in the prediction. Our results showed that we could use Google search queries to predict Zika cases 1 week earlier before the outbreak. These findings are important to help healthcare authorities evaluate the outbreak and take necessary precautions.

scholarly journals Investors’ attention: does it impact the Nigerian stock market activities?

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Osarumwense Osabuohien-Irabor
Keyword(s):  
Stock Market ◽  
Regression Model ◽  
Stock Markets ◽  
Trading Volume ◽  
Stock Return ◽  
Search Query ◽  
Content Type ◽  
Page View ◽  
Information Demand ◽  
Google Search

PurposeThe author investigates whether investors’ online information demand measured by Google search query and the changes in the numbers of Wikipedia page view can explain and predict stock return, trading volume and volatility dynamics of companies listed on the Nigerian Stock Exchange.Design/methodology/approachThe multiple regression model which encompasses both the univariate and multivariate regression framework was employed as the research methodology. As part of our pre-analysis, we test for multicollinearity and applied the Wu/Hausman specification test to detect whether endogeneity exist in the regression model.FindingsWe provide novel and robust evidence that Google searches neither explain the contemporaneous nor predict stock return, trading volume and volatility dynamics. Similarly, results also indicate that trading volume and volatility dynamics have no relationship with changes in the numbers of Wikipedia pages view related to stock activities.Originality/valueThis study opens new strand of empirical literature of “investors' attention” in the context of African stock markets as empirical evidence. No evidence from previous studies on investors' attention exist, whether in Google search query or Wikipedia page view, with respect to African stock markets, particularly the Nigerian stock market. This study seeks to bridge these knowledge gaps by examining these relations.

A transparent legal system of indicators as the indicator of personal fit

2018 ◽  
Vol 26 (1) ◽  
pp. 171-184
Author(s):  
Mirko Pečarič
Keyword(s):  
Rule Of Law ◽  
Public Law ◽  
Point Of View ◽  
Google Trends ◽  
Intangible Capital ◽  
Legal Environment ◽  
Content Type ◽  
The Rule Of Law ◽  
The Public ◽  
Google Search

Purpose Fit and misfit (F&M) affect thoughts, actions and implementation. Both concepts are unknown in the law or in the public administration; so, this paper aims to demonstrate how these concepts can be addressed from the legal point of view. Design/methodology/approach F&M have not yet been addressed from a legal point of view. To determine a connection between them, the rule of law, F&M is compared with the indexes of happiness and life satisfaction. The claim that F&M can be more objectively stated in regulation that must be based on public participation is tested with Google Trends. Google Trends gave data on the searched notions (regulation, participation, organisation and misfit), for which statistical calculations are made to establish relations between them. Findings F&M are an intangible capital with which the rule of law is tightly connected. Citizens are happy and satisfied in countries with a high rank on the rule of law and vice versa. Correlations are positive for the misfit and regulation, participation and organisation, regulation and organisation and regulation and participation, while those for misfit and organisation are low. Google search therefore denies the strongest connection between misfit and organisation that is in the centre of F&M literature. Originality/value F&M have not yet been addressed from a legal point of view, although they have a lot of similarity if not the same. Based on this predisposition, this paper refutes some “romantic” ideas about person–environment and person–organisation fit, and it gives opposite arguments from the public law point of view. The paper tries to point to optimal specificity for fit in a legal environment based on proposed indicators and gives directions for further research.

The traces of ecotourism in a digital world: spatial and trend analysis of geotagged photographs on social media and Google search data for sustainable development

2020 ◽  
Vol 11 (2) ◽  
pp. 183-202
Author(s):  
Hanyoung Go ◽  
Myunghwa Kang ◽  
Yunwoo Nam
Keyword(s):  
Social Media ◽  
Sustainable Development ◽  
Spatial Analysis ◽  
Google Trends ◽  
Internet Search ◽  
Content Type ◽  
Digital World ◽  
Search Data ◽  
Google Search ◽  
Over Time

Purpose This paper aims to track how ecotourism has been presented in a digital world over time using geotagged photographs and internet search data. Ecotourism photographs and Google Trends search data are used to evaluate tourist perceptions of ecotourism by developing a categorization of essential attributes, examining the relation of ecotourism and sustainable development, and measuring the popularity of the ecotourism sites. Design/methodology/approach The researchers collected geotagged photographs from Flickr.com and downloaded Google search data from Google Trends. An integrative approach of content, trend and spatial analysis was applied to develop ecotourism categories and investigate tourist perceptions of ecotourism. First, the authors investigate ecotourism geotagged photographs on a social media to comprehend tourist perceptions of ecotourism by developing a categorization of key ecotourism attributes and measuring the popularity of the ecotourism sites. Second, they examined how ecotourism has been related with sustainable development using internet search data and investigate the trends in search data. Third, spatial analysis using GIS maps was used to visualize the spatial-temporal changes of photographs and tourist views throughout the world. Findings This study identified three primary themes of ecotourism perceptions and 13 categories of ecotourism attributes. Interest over time about ecotourism was mostly presented as its definitions in Google Trends. The result indicates that tracked ecotourism locations and tourist footprints are not congruent with the popular regions of ecotourism Google search. Originality/value This research follows the changing trends in ecotourism over a decade using geotagged photographs and internet search data. The evaluation of the global ecotourism trend provides important insights for global sustainable tourism development and actual tourist perception. Analyzing the trend of ecotourism is a strategic approach to assess the achievement of UN sustainable development goals. Factual perspectives and insights into how tourists are likely to seek and perceive natural attractions are valuable for a range of audiences, such as tourism industries and governments.

Assessing business process complexity based on textual data: Evidence from ITIL IT ticket processing

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Nina Rizun ◽  
Aleksandra Revina ◽  
Vera G. Meister
Keyword(s):  
Information Technology ◽  
Language Processing ◽  
Business Process ◽  
Real World ◽  
Content Type ◽  
Real World Application ◽  
Starting Point ◽  
Two Samples ◽  
Textual Data ◽  
Cognitive Attention

PurposeThis study aims to draw the attention of business process management (BPM) research and practice to the textual data generated in the processes and the potential of meaningful insights extraction. The authors apply standard natural language processing (NLP) approaches to gain valuable knowledge in the form of business process (BP) complexity concept suggested in the study. It is built on the objective, subjective and meta-knowledge extracted from the BP textual data and encompassing semantics, syntax and stylistics. As a result, the authors aim to create awareness about cognitive, attention and reading efforts forming the textual data-based BP complexity. The concept serves as a basis for the development of various decision-support solutions for BP workers.Design/methodology/approachThe starting point is an investigation of the complexity concept in the BPM literature to develop an understanding of the related complexity research and to put the textual data-based BP complexity in its context. Afterward, utilizing the linguistic foundations and the theory of situation awareness (SA), the concept is empirically developed and evaluated in a real-world application case using qualitative interview-based and quantitative data-based methods.FindingsIn the practical, real-world application, the authors confirmed that BP textual data could be used to predict BP complexity from the semantic, syntactic and stylistic viewpoints. The authors were able to prove the value of this knowledge about the BP complexity formed based on the (1) professional contextual experience of the BP worker enriched by the awareness of cognitive efforts required for BP execution (objective knowledge), (2) business emotions enriched by attention efforts (subjective knowledge) and (3) quality of the text, i.e. professionalism, expertise and stress level of the text author, enriched by reading efforts (meta-knowledge). In particular, the BP complexity concept has been applied to an industrial example of Information Technology Infrastructure Library (ITIL) change management (CHM) Information Technology (IT) ticket processing. The authors used IT ticket texts from two samples of 28,157 and 4,625 tickets as the basis for the analysis. The authors evaluated the concept with the help of manually labeled tickets and a rule-based approach using historical ticket execution data. Having a recommendation character, the results showed to be useful in creating awareness regarding cognitive, attention and reading efforts for ITIL CHM BP workers coordinating the IT ticket processing.Originality/valueWhile aiming to draw attention to those valuable insights inherent in BP textual data, the authors propose an unconventional approach to BP complexity definition through the lens of textual data. Hereby, the authors address the challenges specified by BPM researchers, i.e. focus on semantics in the development of vocabularies and organization- and sector-specific adaptation of standard NLP techniques.

scholarly journals A review of the trend of microlearning

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Kelvin Leong ◽  
Anna Sung ◽  
David Au ◽  
Claire Blanchard
Keyword(s):  
Language Learning ◽  
Real World ◽  
Google Trends ◽  
Real World Data ◽  
Content Type ◽  
World Data ◽  
E Learning ◽  
Internet Searches ◽  
Relevant Publication ◽  
The Right

PurposeMicrolearning has been considered as a promising topic in work-based learning. This paper aims to review the trends of microlearning in terms of related publications and Internet searches. Hopefully, the findings can serve as a reference for the education sector, government, business and academia to promote, design and use microlearning.Design/methodology/approachIn this study, two sets of analysis were conducted. Firstly, the authors analysed the publication trend of microlearning. Second, the authors analysed the trend of Internet searches related to microlearning. More specifically, the authors analysed real-world data of 14 years obtained from Scopus and Google Trends for the purpose. These data include the first relevant publication found in the database.FindingsIn total, 476 relevant publications have been identified during 2006–2019. According to the findings from the analysis of the identified publications, microlearning is a relevantly new and emerging global topic involving authors, affiliations and funding sponsors from different countries. Moreover, many microlearning-related publications were conducted from perspectives of e-learning or mobile learning. Furthermore, the authors notice higher education was the most frequently mentioned education level in the identified publications. On the other hand, language learning (i.e. second language, vocabulary learning, etc.) had been mentioned more times in the titles and abstracts than other subject areas. Overall, the increasing trend of publications on “microlearning” (as a knowledge supply) is in line with the established increasing Internet searches of “microlearning” (as a practical demand) in recent years.Practical implicationsFrom the work-based learning perspective, microlearning has been considered as one of the key topics in talent development topics. Policymakers, educators, researchers and participators have the responsibility to explore how to promote, design and use microlearning to help people to learn in the right direction through valid knowledge with ethical consideration.Originality/valueAlthough many works had been done on microlearning, there is a lack of comprehensive studies reviewing the trends of microlearning in terms of related publications and Internet searches. This study aims to fill this gap by analysing real-world data obtained from Scopus and Google Trends – these data include the first relevant publication found in the database. The authors believe this is the first time that a study has been conducted to comprehensively review the development trends of microlearning. Hopefully, this study can shed some light on related research.

1243-P: Novel Use of Natural Language Processing to Identify Reasons for Insulin Discontinuation in Patients with T2DM: A Real-World Evidence Study

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 1243-P
Author(s):  
JIANMIN WU ◽  
FRITHA J. MORRISON ◽  
ZHENXIANG ZHAO ◽  
XUANYAO HE ◽  
MARIA SHUBINA ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Real World ◽  
Real World Evidence

scholarly journals Google, Tell Me. Is He Gay? Masculinity, Heterosexuality, and Gendered Anxieties in Google Search Queries about Sexuality

2021 ◽  
pp. 073112142110019
Author(s):  
Emma Mishel ◽  
Tristan Bridges ◽  
Mónica L. Caudillo
Keyword(s):  
Social Desirability ◽  
Google Trends ◽  
Social Desirability Bias ◽  
Same Sex ◽  
Search Queries ◽  
Sexual Identities ◽  
Google Search

It is difficult to gauge people’s acceptance about same-sex sexualities, as responses to questionnaires are prone to social desirability bias. We offer a new proxy for understanding popular concern surrounding same-sex sexualities: prevalence of Google searches demonstrating concern over gay/lesbian sexual identities. Using Google Trends data, we find that Google searches about whether a specific person is gay or lesbian show patterned bias toward masculine searches, in that such searches are much more frequently conducted about boys and men compared with girls and women. We put these findings into context by comparing search frequencies with other popular Google searches about sexuality and otherwise. We put forth that the patterned bias toward masculine searches illustrates support for the enduring relationship between masculinity and heterosexuality and that it does so on a larger scale than previous research has been able to establish.

Sign in / Sign up

Export Citation Format

Share Document