README.Rmd

---
title: "Using text mining in systematic literature reviews"
output: github_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

This page was created to reproduce the results of the paper: 

> Valença, G.; Moura, F.; Morais de Sá, A. (2023). How can we develop road space allocation solutions for smart cities using emerging information technologies? A review using text mining. International Journal of Information Management Data Insights, 3(1), 100150. https://doi.org/10.1016/j.jjimei.2022.100150 

Also, this material provides the code and guidelines that explain step by step on performing text mining in systematic literature reviews. The code is reproducible and can be replicated to other literature reviews regardless of the field of study. Please **cite** the paper above, in case you use any of the material. 

#### 1. Dataset

* [Excel sheet with main information of all collected papers and filtering process.](/Data/table_systematic_review.xlsx)

* [Full eligible papers used for the topic modelling and bigrams in *.txt](/Data/Full_papers/)  


#### 2. Guidelines 

* [Historical Context Analysis](Historical_Context_Abs_TM.md): Analysis of how the concepts and topics evolve throughout time by using word frequency counts in abstracts. 

* [Specific Paper Analysis](LDA_Bigrams_Full_Papers.md): Detecting the main topics expressed in the literature by using topic modeling and bigrams in full papers.

#### 3. Code

The full scripts used for producing all the results are found in:

* [Historical Context Analysis](/Scripts/Script_Historical_Analysis.R)

* [Specific Paper Analysis](/Scripts/Script_Specific_Analysis.R)