Sentiment Analysis Based on 100 Doctoral Acknowledgments
Bibliographic record
Abstract
This paper conducts sentiment analysis and LDA topic model on 100 doctoral thesis acknowledgement texts to explore the changes in emotional inclination and attitudes towards different subjects, events, and objects in the process of acknowledgement writing. The aim is to build a doctoral thesis acknowledgement corpus by collecting 28,152 segmented words and establish a time span of 2017-2024. The results are as follows: (1) There are five types of high-frequency words in doctoral acknowledgements: important interpersonal relationships (children, parents, etc.), key actions, focus objects, critical processes, and psychological feelings; (2) On a statistical level, the positive emotions in doctoral acknowledgements are significantly greater than the negative emotions. However, considerable negative emotions need to be paid attention to, such as anxiety and guilt; (3) The positive topics of doctoral acknowledgements include support and companionship, guidance and assistance, life, academic and school, and personal growth. The negative themes include thesis writing, research experience, mentor-disciple relationship, and institutional assistance.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.006 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".