Text Mining over Curriculum Vitae of Peruvian Professionals using Official Scientific Site DINA

Chire, Josimar; Apaza, Honorio

doi:10.1109/ics51289.2020.00030

Cited by 2 publications

(2 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The academic paper is related to other research [18], since text mining tools such as the use of R libraries, tm package and WordCloud were used to form the word cloud and to verify the words frequency. On the other hand, four steps were used in the methodology, of which the preprocessing and data visualization was used in the present study [19].…”

Section: Discussionmentioning

confidence: 99%

“…Applications of TM are widespread and diverse, including cancer research analysis [9], opinion analysis of digital banking applications [10], sentimental analysis [11,12] vaccine hesitancy studies [13], and analysis of information management systems research topics [14]. Other applications include Twitter content sharing studies [15], text classification [16], database vulnerability identification [17], Business Intelligence article reviews [18], Peruvian professional CV analysis [19], and construction sector research [20].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Leveraging Text Mining for Analyzing Students' Preferences in Computer Science and Language Courses

Llanos,

Huatangari,

Meza

et al. 2023

ISI

View full text Add to dashboard Cite

In an increasingly competitive, globalized world, educational institutions must strategically offer courses that align with the skill-acquisition needs of their target audience. As such, the application of text mining techniques to extract valuable insights and patterns from structured data across various knowledge domains becomes paramount. This study employed text mining to scrutinize students' preferences for course offerings at the Computer and Language Center of the National University of Jaen. The analysis was based on data collected from a Google Forms survey of 315 students. The employed methodology facilitated the unearthing of patterns, trends, and semantic relationships within a large corpus of students' opinions. Frequency distributions and word clouds were generated using R programming language. Furthermore, the WEKA software and Python were utilized for cluster analysis, enabling the detection of groupings and trends within the data. Although other methods such as sentiment analysis and statistical methodologies exist, text mining was deemed most suitable for identifying patterns and relationships within students' opinions. The study revealed that students predominantly favored advanced Excel, AutoCAD, ArcGIS, Nutrition, and Revit courses, which appeared to correlate with their professional aspirations and prevailing course trends. Therefore, the application of text mining tools to analyze structured institutional data can significantly contribute to informed decision-making processes.

show abstract

Section: Discussionmentioning

confidence: 99%