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Sentiment analysis of students’ feedback using machine learning algorithms has emerged as a valuable tool for understanding students’ sentiments and improving educational outcomes. Currently, existing systems use frequency-based methods for feature selection (e.g., Term Frequency-Inverse Document Frequency (TF-IDF) and Bag of Words (BoW)) not to capture the subtleties of emotions expressed in student feedback and do not provide insights into the specific concerns of students via topics or themes. In this study, we propose the Student Sentiment from Feedback (SSF) framework, which includes four main procedures: pre-processing, feature selection, classification, and theme finding. The SSF framework classifies student sentiments and subsequently groups feedback into themes using semantic networks based on word co-occurrence. Our innovative feature selection approach combines TF-IDF with sentiment-based features derived from SentiWordNet and intensifiers, creating a robust feature vector that enhances the dataset’s richness and improves classification accuracy and robustness. In the experiments, we utilize a public dataset from Kaggle, applying our proposed method and various machine learning models (e.g., k-nearest neighbor, decision tree, random forest, multilayer perceptron, support vector machine, gradient boosting, and extreme gradient boosting). The experimental results show that our concatenated features achieve the highest accuracy across all machine learning models (greater than 0.82). Our study demonstrates the efficacy of this hybrid feature selection method, contributing to better understanding and decision-making in educational settings.

Aim: Cancers lacking standard screening (LSS) options account for approximately 70% of cancer-related deaths due to late-stage diagnosis. Circulating tumor DNA (ctDNA) is a promising biomarker for multi-cancer early detection. We previously developed SPOT-MAS, a multimodal ctDNA-based assay analyzing methylation and fragmentomic profiles, effective in detecting common cancers (breast, colorectal, liver, lung and gastric). This study extends the analysis to five LSS cancers: endometrial, esophageal, head and neck, ovarian and pancreatic. Methods: SPOT-MAS was applied to profile cfDNA methylation and fragmentomic patterns in 739 healthy individuals and 135 LSS cancer patients. Results: We identified 347 differentially methylated regions and observed genome-wide hypomethylation across all five LSS cancers. Esophageal and head and neck cancers showed an enrichment of short cfDNA fragments (<150 bp). Eleven 4-mer end motifs were consistently altered in cfDNA fragments across all LSS cancers. Many significant signatures were consistent with previous observations in common cancers. Notably, SPOT-MAS achieved 96.2% specificity and 74.8% overall sensitivity, with a lower sensitivity of 60.7% in early-stage cancers. Conclusion: This proof-of-concept study demonstrates that SPOT-MAS a non-invasive test trained on five common cancer types, could detect a number of LSS cancer cases, potentially complementing existing screening programs.