Computing Consumer Sentiment in Germany via Social Media Data

Deniz Karaman Örsal; Silke Sturm

Computing Consumer Sentiment in Germany via Social Media Data

Publikation: Arbeits- oder Diskussionspapiere und Berichte › Arbeits- oder Diskussionspapiere

Standard

Computing Consumer Sentiment in Germany via Social Media Data. / Karaman Örsal, Deniz; Sturm, Silke.
Hamburg: Universität Hamburg, 2021. (Hamburg Discussion Papers in International Economics; Nr. 7).

Publikation: Arbeits- oder Diskussionspapiere und Berichte › Arbeits- oder Diskussionspapiere

Harvard

Karaman Örsal, D & Sturm, S 2021 'Computing Consumer Sentiment in Germany via Social Media Data' Hamburg Discussion Papers in International Economics, Nr. 7, Universität Hamburg, Hamburg. <http://hdl.handle.net/10419/229183>

APA

Karaman Örsal, D., & Sturm, S. (2021). Computing Consumer Sentiment in Germany via Social Media Data. (Hamburg Discussion Papers in International Economics; Nr. 7). Universität Hamburg. http://hdl.handle.net/10419/229183

Vancouver

Karaman Örsal D, Sturm S. Computing Consumer Sentiment in Germany via Social Media Data. Hamburg: Universität Hamburg. 2021. (Hamburg Discussion Papers in International Economics; 7).

Bibtex

@techreport{3497279c3ba3481983728be83b427d83,

title = "Computing Consumer Sentiment in Germany via Social Media Data",

abstract = "Survey-based consumer confidence indicators are mostly reported with adelay and are a result of time consuming and expensive consumer surveys.In this study, to measure the current consumer confidence in Germany, wedevelop an approach, in which we compute the consumer sentiment usingpublic Tweets from Germany. To achieve this goal we develop a new sentimentscore. To measure the consumer sentiment, we use text-mining tools and publicTweets from May 2019 to August 2020. Our findings indicate that there is ahigh correlation between the consumer confidence indicator based on surveydata, and the consumer sentiment that we compute using data from Twitterplatform. With our approach, we are even able to forecast the change in nextmonth{\textquoteright}s consumer confidence.",

keywords = "Economics, consumer sentiment, consumer confidence, twitter, sentiment analysis",

author = "{Karaman {\"O}rsal}, Deniz and Silke Sturm",

year = "2021",

language = "English",

series = "Hamburg Discussion Papers in International Economics",

publisher = "Universit{\"a}t Hamburg",

number = "7",

address = "Germany",

type = "WorkingPaper",

institution = "Universit{\"a}t Hamburg",

}

RIS

TY - UNPB

T1 - Computing Consumer Sentiment in Germany via Social Media Data

AU - Karaman Örsal, Deniz

AU - Sturm, Silke

PY - 2021

Y1 - 2021

N2 - Survey-based consumer confidence indicators are mostly reported with adelay and are a result of time consuming and expensive consumer surveys.In this study, to measure the current consumer confidence in Germany, wedevelop an approach, in which we compute the consumer sentiment usingpublic Tweets from Germany. To achieve this goal we develop a new sentimentscore. To measure the consumer sentiment, we use text-mining tools and publicTweets from May 2019 to August 2020. Our findings indicate that there is ahigh correlation between the consumer confidence indicator based on surveydata, and the consumer sentiment that we compute using data from Twitterplatform. With our approach, we are even able to forecast the change in nextmonth’s consumer confidence.

AB - Survey-based consumer confidence indicators are mostly reported with adelay and are a result of time consuming and expensive consumer surveys.In this study, to measure the current consumer confidence in Germany, wedevelop an approach, in which we compute the consumer sentiment usingpublic Tweets from Germany. To achieve this goal we develop a new sentimentscore. To measure the consumer sentiment, we use text-mining tools and publicTweets from May 2019 to August 2020. Our findings indicate that there is ahigh correlation between the consumer confidence indicator based on surveydata, and the consumer sentiment that we compute using data from Twitterplatform. With our approach, we are even able to forecast the change in nextmonth’s consumer confidence.

KW - Economics

KW - consumer sentiment

KW - consumer confidence

KW - twitter

KW - sentiment analysis

UR - https://www.econstor.eu/bitstream/10419/229183/1/hdpie-no07.pdf

M3 - Working papers

T3 - Hamburg Discussion Papers in International Economics

BT - Computing Consumer Sentiment in Germany via Social Media Data

PB - Universität Hamburg

CY - Hamburg

ER -