State of the Art Research on Sustainable Use of Water Hyacinth: A Bibliometric and Text Mining Analysis
Bibliographic record
Abstract
This study aims to present a systematic data-driven bibliometric analysis of the water hyacinth (Eichhornia crassipes) infestation problem around the globe. As many solutions are being proposed in academia for its management, mitigation, and utilization, it requires investigation through a systematic scrutinizing lens. In this study, literature records from 1977 to June 2020 concerning research on water hyacinth are taken from Scopus for text analysis. Trends in the publication of different article types, dynamics of publication, clustering, correlation, and co-authoring patterns between different countries are observed. The cluster analysis indicated four clusters viz. (i) ecological works related to species, (ii) pollutant removal process and methods, (iii) utilization of biofuels for biogas production, and (iv) modelling works. It is clear from the networking analysis that most of the publications regarding water hyacinth are from India, followed by China and the United States. Sentiment analysis with the AFINN lexicon showed that the negative sentiment towards the aquatic weed has intensified over time. An exploratory analysis was performed using a bigram network plot, depicting and outlining different important domains of water hyacinth research. Water hyacinth research has passed the pioneering phase and is now at the end of a steady growth phase or at the beginning of an acceleration phase. In this article, an overview is given for the entirety of water hyacinth research, with an indication of future trends and possibilities.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.139 | 0.163 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".