EmoAtlas: An emotional network analyser of texts merging psychological lexicons, artificial intelligence and network science
Bibliographic record
Abstract
We introduce EmoAtlas, a computational library/framework extracting emotions and syntactic/semantic word associations from texts. EmoAtlas combines interpretable artificial intelligence (AI) for syntactic parsing in 18 languages and psychologically-validated lexicons for detecting the 8 emotions in Plutchik's theory. We show that EmoAtlas can match or surpass transformer-based natural language processing techniques, BERT or Large Language Models like ChatGPT 3.5 or LLaMantino, in detecting emotions from Italian and English online posts and news articles (e.g., achieving 85.6$\%$ accuracy in detecting anger in posts vs the 68.8$\%$ value of ChatGPT and 89.9\% value for BERT). EmoAtlas presents important advantages in terms of speed and absence of fine-tuning, e.g. it runs 12x faster than BERT on the same data. Testing EmoAtlas' and easily trainable transformers' relevance in a psychometric task like reproducing human creativity ratings for 1,071 short texts, we find that EmoAtlas and BERT obtain equivalent predictive power (4-fold cross-validation, $\rho \approx 0.495$, $p<10^{-4}$). Combining BERT's semantic features with EmoAtlas' emotional/syntactic networks of words gets substantially better at estimating creativity rates of stories ($\rho=0.628$, $p<10^{-4}$). This indicates an interplay between the creativity of narratives and their semantic, emotional, and syntactic structure. Our results provides a quantitative example about how EmoAtlas and transformers could be used in synergy in psychometrics. Via interpretable emotional profiles and syntactic networks, EmoAtlas can quantify how emotions are channelled through specific associations in texts, e.g., how did customers frame their ideas and emotions towards "beds" in hotel reviews? We release EmoAtlas as a standalone "text as data" computational tool and discuss its impact in extracting interpretable and reproducible insights from texts.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".