Marketing Research: The Role Of Sentiment Analysis
AbstractThis article promotes sentiment analysis as an alternative research technique for collecting and analyzing textual data on the internet. Sentiment analysis is a data mining technique that systematically evaluates textual content using machine learning techniques. As a research method in marketing, sentiment analysis presents an efficient and effective evaluation of consumer opinions in real time. It allows data collection and analysis from a very large sample without hindrances, obstructions and time delays. Through sentiment analysis, marketers collect rich data on attitudes and opinion in real time, without compromising reliability, validity and generalizability. Marketers also gather feedback on attitudes and opinions as they occur without having to invest in lengthy and costly market research activities. The paper proposes sentiment analysis as an alternative technique capable of triangulating qualitative and quantitative methods through innovative real time data collection and analysis. The paper concludes with the challenges marketers can face when using this technique in their research work.
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Bibliographic InfoPaper provided by Universidade do Porto, Faculdade de Economia do Porto in its series FEP Working Papers with number 489.
Length: 21 pages
Date of creation: Apr 2013
Date of revision:
Sentiment analysis; Machine leaning; Marketing research; Triangulation; Qualitative research; Quantitative research;
Find related papers by JEL classification:
- M10 - Business Administration and Business Economics; Marketing; Accounting - - Business Administration - - - General
- M30 - Business Administration and Business Economics; Marketing; Accounting - - Marketing and Advertising - - - General
- M31 - Business Administration and Business Economics; Marketing; Accounting - - Marketing and Advertising - - - Marketing
- M39 - Business Administration and Business Economics; Marketing; Accounting - - Marketing and Advertising - - - Other
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- Gruen, Thomas W. & Osmonbekov, Talai & Czaplewski, Andrew J., 2006. "eWOM: The impact of customer-to-customer online know-how exchange on customer value and loyalty," Journal of Business Research, Elsevier, vol. 59(4), pages 449-456, April.
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