Emotions and sentiment: An exploration of artist websites
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".