Library Chat Transcript Evaluation for User Sentiment During the COVID-19 Pandemic
Bibliographic record
Abstract
Objective – The purpose of this research was to explore user sentiment on Ask a Librarian, a consortial chat service for university libraries in Ontario, Canada, between 2019 to 2021. We tested how the characteristics of the chat (such as year, semester, user type, operator type, affiliation mismatch, and user complaints) and the onset of the COVID-19 pandemic affected sentiment scores. Methods – The researchers analyzed 3,339 chat transcripts using VADER, a free, open-source Python natural language processing library for sentiment analysis. We tested the significance of relationships between study variables and sentiment score using either a two-samples t-test or ANOVA. Results – Between 2019 to 2021, overall sentiment on Ask a Librarian was positive and higher among operators than users. There was a significant relationship between sentiment scores and operator type, affiliation mismatch, and complaints respectively. The year, semester, and pandemic status of the chat were also significantly associated with sentiment score. Chats that took place during the COVID-19 pandemic had a significantly higher overall sentiment score than pre-pandemic chats. Average user sentiment score was also higher during the pandemic, but there were no significant differences in average operator sentiment score. Conclusion – The COVID-19 pandemic had a significant effect on the emotional tone of the overall chat interaction, as well as the sentiment within the user’s messages. Practitioners can replicate our approach to understand user emotions, opinions, attitudes, or appraisals during times of disruption or emergency, as well as for regular service assessment.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".