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Emotions and sentiment: An exploration of artist websites
Author(s) -
Pitt Christine,
Kietzmann Jan,
Botha Elsamari,
Wallström Åsa
Publication year - 2018
Publication title -
journal of public affairs
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.221
H-Index - 20
eISSN - 1479-1854
pISSN - 1472-3891
DOI - 10.1002/pa.1653
Subject(s) - disappointment , sentiment analysis , remorse , set (abstract data type) , consumption (sociology) , psychology , advertising , computer science , social psychology , sociology , business , artificial intelligence , social science , programming language
Artists of all genres express their emotions through their creations and market their works online. We argue that in marketing their work online, it is important to understand not only the emotional responses of the artistic works themselves but also that the sentiment evoked on their websites matters. Developing the correct website sentiment can have favorable consequences. It can increase the interest of potential consumers, assure that appropriate expectations are set for the actual consumption experience, and lead to increased sales and word of mouth marketing. Online sentiment that is ill‐aligned to the emotions the actual offering evokes can have adverse consequences, including disappointment with the actual offering and buyer's remorse. To better understand the online sentiment of artists' websites, we begin by briefly revisiting the interplay between art, emotions, and the issue of online “sentiment.” Then, we describe a study of a sample of artists' websites that had the objective of gauging both the nature of and the extent of the emotions present in its text, as well as gaining an indication of the sentiment of the website. We describe the use of a relatively new content analysis tool to do this. Following this, we explore the data gathered, with the specific purpose of determining whether the emptions expressed on artists' websites can significantly predict sentiment, if so, which emotions tend to be the strongest predictors. We conclude by discussing some managerial implications of the results and by identifying avenues for future research.