This repository hosts the top2vec_scientific_texts model, a specialized Top2Vec model trained on scientific texts for topic modeling and semantic search.
Model Overview
The top2vec_scientific_texts model is built for analyzing scientific literature. It leverages the Universal Sentence Encoder for embedding texts and uses Top2Vec for topic modeling.
Key Features:
Domain-Specific: Tailored for scientific texts.
Base Model: Utilizes the Universal Sentence Encoder for effective text embeddings.
Topic Modeling: Employs Top2Vec for discovering topics in scientific documents.
Installation
To use the model, you need to install the following dependencies:
The entire process of model training, dataset creation, and visualization is documented in the main.ipynb Jupyter notebook. To explore the code and replicate the results:
Open the main.ipynb notebook in Jupyter Lab or Jupyter Notebook.
Execute the cells in sequence to run different stages of the analysis.
The results, including thematic group analysis, trend analysis, and visualizations of interest dynamics over the years, are presented in the form of tables and graphs within the notebook.
For more details, please refer to the main.ipynb notebook in this repository.
Usage
Here's an example of how to use the model for topic modeling:
The model was trained on a dataset of scientific abstracts sourced from arXiv. The dataset covers a range of topics within the field of computer science from 2010 to 2024.
The top2vec_scientific_texts model can be used for various purposes, including:
Topic Discovery: Identify the main topics within a collection of scientific texts.
Semantic Search: Find documents that are semantically similar to a query text.
Trend Analysis: Analyze the evolution of topics over time.
Examples
Here are some examples of the model's output for the thematic group "UAV in Disasters and Emergency":
Trend Analysis for "UAV in Disasters and Emergency"
Trend Analysis
This graph shows the trend of interest in the use of UAVs in disaster and emergency situations over time.
Key Metrics Table
Analysis for Thematic Group: Disasters & Emergency
Year
Number of Publications
Growth Acceleration
Change in Number of Publications
Relative Growth
2010
19
0
0
0.0%
2011
15
-4
-4
-21.05%
2012
28
17
13
86.67%
2013
38
-3
10
35.71%
2014
28
-20
-10
-26.32%
2015
47
29
19
67.86%
2016
63
-3
16
34.04%
2017
94
15
31
49.21%
2018
173
48
79
84.04%
2019
266
14
93
53.76%
2020
337
-22
71
26.69%
2021
380
-28
43
12.76%
2022
453
30
73
19.21%
2023
509
-17
56
12.36%
Contributions
We welcome contributions to the top2vec_scientific_texts model. If you have suggestions, improvements, or encounter any issues, please feel free to open an issue or submit a pull request.
