Text Mining of Journal Article Titles: An LDA-Based Topic Modeling Approach
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
Among the techniques of text mining, topic modeling is considered one of the emerging tools to extract or detect hidden themes that lie within a huge collection of textual data. Latent Dirichlet Allocation (LDA) is considered a popular method in the field of topic modeling. This paper deals with topic modeling from 9130 articles of Sri Lankan authors having a minimum of 5 citations downloaded from the WoS database using LDA. The LDA tuning (R package) is used in the study to take various measurements for deciding subjects in light of factual elements. The top 10 latent topics were identified, and different unique terms associated with the topics were also discussed. Health is traced as the most occurring latent topic followed by forest and solar cells. Topic-1 (100%) Contains Water-related terms, which is around 60%; Irrigation and soilrelated were 40% (1997). This first topic was prominent across the period barring 1994 and 1996. Topic 3 has gradually decreased and Topic 9 has gradually increased during the last five decades. By comparing our results to traditional scholarship by Sri Lankan authors and the evolution of scientific publication by the island nation, we have shown that topic models can emerge as a scientific alternative to conventional classification systems.
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 | 0.002 | 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 | 0.001 |
| 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