Emotions and sentiment: An exploration of artist websites
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it