Text mining analysis on students’ expectations and anxieties towards data analytics course

Bringula, Rex P.; Ulfa, Saida; Miranda, John Paul P.; Atienza, Francis Arlando L

doi:10.1080/23311916.2022.2127469

Cited by 5 publications

(3 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our team already used data mining to identify the daily-living experience of teenagers suffering from Tourette syndrome [ 63 ]. In education [ 33 , 64 , 65 ], data mining can be useful for student evaluation [ 66 ], or for determining student satisfaction with a teaching module [ 67 , 68 ]. Text-mining has also been used to provide a better understanding of curricula offered by many higher education institutions thereby helping students decide which one they want to join [ 69 ].…”

Section: Discussionmentioning

confidence: 99%

A podcast to teach medical humanities at medical school: a text-mining study of students’ lived experience

Roze,

Nilles,

Louapre

et al. 2024

Medical Education Online

View full text Add to dashboard Cite

Section: Discussionmentioning

confidence: 99%

A podcast to teach medical humanities at medical school: a text-mining study of students’ lived experience

Roze,

Nilles,

Louapre

et al. 2024

Medical Education Online

View full text Add to dashboard Cite

“…Several studies on sentiment analysis or text classification in the Indonesian language do not use negation handling [2], [3], [49]- [51]. On average, these studies only focus on word representation and the sentiment classification methods used.…”

Section: Negation Handling In Indonesian Sentiment Analysis Tasksmentioning

confidence: 99%

Negation handling for sentiment analysis task: approaches and performance analysis

Ilmawan,

Muladi,

Prasetya

2024

IJECE

View full text Add to dashboard Cite

Negation plays an essential role in sentiment analysis within natural language processing (NLP). Its integration involves two key aspects: identifying the scope of negation and incorporating this information into the sentiment model. Before delving into scope detection, the specific negation cue must be identified, with explicit and implicit negation cues being the two main types. Various methodologies, such as rule-based, machine learning, and hybrid approaches, address the negation scope detection challenge. Strategies for leveraging negation information in sentiment models encompass heuristic polarity modification, feature space augmentation, end-to-end approach, and hierarchical multi-task learning. Notably, there is a need for more studies addressing implicit negation cue detection, even within the state-of-the-art bidirectional encoder representation for transformers (BERT) approach. Some studies have employed reinforcement learning and hybrid techniques to address the implicit negation problem. Further exploration, particularly through a hybrid and multi-task learning approach, is warranted to make potential contributions to the nuanced challenges of handling negation in sentiment analysis, especially in complex sentence structures.

show abstract

“…The text mining methods used in this study were adapted from the studies of Bringula et al (2022). Figure 1 shows the preprocessing steps performed on the responses to have them ready for NLP.…”

Section: Data Preprocessing Cleaning and Processingmentioning

confidence: 99%

Utilization of Writing Assistance Tools in Research in Selected Higher Learning Institutions in the Philippines: A Text Mining Analysis

Santiago,

Embang,

Acanto

et al. 2023

IJLTER

View full text Add to dashboard Cite

The rise of artificial intelligence (AI) in higher education has sparked significant interest. Artificial intelligence offers opportunities for global learning, personalized instruction, and efficient resource management. In research writing, AI tools streamline processes, from a literature review to data analysis, enhancing efficiency and freeing up time for critical thinking. The researchers of this study used text mining techniques to extract patterns and trends of using writing assistance tools in research from the responses of 327 faculty researchers in various higher learning institutions in the Philippines. Unigram tokenization was used to present the 10 most frequently used words in the corpus, while the k-means elbow method was utilized to show the optimal number of clusters. Sentiment analysis was use to show the positive and negative implications of using writing assistance tools in research. The finding is that faculty respondents value writing assistance tools for enhancing research writing by expediting processes and improving clarity. The implementation of these tools in selected higher learning institutions may present difficulties, such as issues related to overreliance, and hinder students’ and researchers’ development of critical thinking and writing skills. Along with technological and resource challenges, these tools could inadvertently encourage plagiarism if not used responsibly. Cultural and language considerations are also relevant because these tools may not always be attuned to the specific linguistic and cultural nuances of Filipino academic writing. The results of this research may serve as a guide for researchers, educators, and software developers, who may use AI to streamline research writing processes in the educational sector.

show abstract

Text mining analysis on students’ expectations and anxieties towards data analytics course

Cited by 5 publications

References 36 publications

A podcast to teach medical humanities at medical school: a text-mining study of students’ lived experience

A podcast to teach medical humanities at medical school: a text-mining study of students’ lived experience

Negation handling for sentiment analysis task: approaches and performance analysis

Utilization of Writing Assistance Tools in Research in Selected Higher Learning Institutions in the Philippines: A Text Mining Analysis

Contact Info

Product

Resources

About