This project focuses on automating the analysis and reporting of bibliometric data, specifically targeting the annual production of academic articles. The primary goal is to understand trends, anomalies, and patterns in bibliometric data through a combination of statistical modeling and exploratory data analysis.
- Regression Modeling: Apply various regression models to identify and analyze publication trends over time.
- Exploratory Data Analysis (EDA): Use EDA techniques to detect anomalies, outliers, and key patterns within the data.
- Trending and Periodic Analysis: Conduct trending and periodic analyses to explore cyclical patterns and predict future publication behaviors.
- Automated Reports: Generate comprehensive bibliometric reports that include graphs, CSV data, and statistical insights.
- Scopus .bib File Integration: Leverage Bibliometrix packages to analyze bibliometric data sourced from Scopus .bib files.
- LLM-Powered PDF Analysis: Incorporate large language models (LLMs) to analyze PDFs, enabling enriched literature reviews and the combination of insights from multiple sources.
- Comprehensive Literature Review PDF: Combine bibliometric analysis and PDF content insights into a cohesive literature review document.
- Bibliometrixs R Integration
- Module 1: Main Information
- Module 2: Annual Articles Production Trend
- Module 3: Authors
- Module 4: Documents
- Module 5: Clustering
- Module 6: Conceptual Structure
- Module 7: Social Structure
- Module 8: Bibliometric Report
- Module 9: ChatGPT Integration Report
- Module 10: ChatGPT Zotero Integration Report
- Module 11: ChatGPT Bunch of PDFs Integration Report
- Module 12: Automatic LaTex Paper Report
The project aims to streamline the literature review process by automating the generation of insightful bibliometric reports. By integrating analysis from bibliometric data and academic papers, the project seeks to provide researchers with a powerful tool for conducting literature reviews efficiently.