Weighted Lexicon-based Sentiment Analysis for Women Career Traits in Information Technology
Bibliographic record
Abstract
In this paper, an unsupervised sentiment analysis model leveraging the Polarity Rank algorithm and sentence dependency graph is proposed to predict the sentiments of women in IT. The primary objective is to identify groups of individuals with common traits (community and job role) who had different sentiments, as well as explore the impact of Diversity Equity & Inclusion (DEI) programs on participants' career satisfaction. The model, based on a 7-point Likert scale, classified responses into Very Negative, Negative, Slightly Negative, Neutral, Slightly Positive, Positive, and Very Positive classes By delving into different demographics and different questions within the survey, it was found that DEI programs have a positive impact on career satisfaction as well as lessening the presence of Anger, Fear, and Sadness. Additionally, it was discovered that Anticipation was a dominant emotion in all responses. This paper provides a foundational look at the sentiments expressed by women in the IT industry.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".