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Between History and Natural Language Processing: Study, Enrichment and Online Publication of French Parliamentary Debates of the Early Third Republic (1881-1899)
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In: ParlaCLARIN III at LREC2022 - Workshop on Creating, Enriching and Using Parliamentary Corpora ; https://hal.archives-ouvertes.fr/hal-03623351 ; ParlaCLARIN III at LREC2022 - Workshop on Creating, Enriching and Using Parliamentary Corpora, Jun 2022, Marseille, France ; https://www.clarin.eu/ParlaCLARIN-III (2022)
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| 2 |
Chinese-Uyghur Bilingual Lexicon Extraction Based on Weak Supervision
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In: Information; Volume 13; Issue 4; Pages: 175 (2022)
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| 3 |
Investigating the Efficient Use of Word Embedding with Neural-Topic Models for Interpretable Topics from Short Texts
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In: Sensors; Volume 22; Issue 3; Pages: 852 (2022)
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| 4 |
Analysis of the Effects of Lockdown on Staff and Students at Universities in Spain and Colombia Using Natural Language Processing Techniques
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In: International Journal of Environmental Research and Public Health; Volume 19; Issue 9; Pages: 5705 (2022)
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| 5 |
An Enhanced Neural Word Embedding Model for Transfer Learning
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In: Applied Sciences; Volume 12; Issue 6; Pages: 2848 (2022)
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| 6 |
Deep Sentiment Analysis Using CNN-LSTM Architecture of English and Roman Urdu Text Shared in Social Media
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In: Applied Sciences; Volume 12; Issue 5; Pages: 2694 (2022)
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| 7 |
Predicting Academic Performance: Analysis of Students’ Mental Health Condition from Social Media Interactions
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In: Behavioral Sciences; Volume 12; Issue 4; Pages: 87 (2022)
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Abstract:
Social media have become an indispensable part of peoples’ daily lives. Research suggests that interactions on social media partly exhibit individuals’ personality, sentiment, and behavior. In this study, we examine the association between students’ mental health and psychological attributes derived from social media interactions and academic performance. We build a classification model where students’ psychological attributes and mental health issues will be predicted from their social media interactions. Then, students’ academic performance will be identified from their predicted psychological attributes and mental health issues in the previous level. Firstly, we select samples by using judgmental sampling technique and collect the textual content from students’ Facebook news feeds. Then, we derive feature vectors using MPNet (Masked and Permuted Pre-training for Language Understanding), which is one of the latest pre-trained sentence transformer models. Secondly, we find two different levels of correlations: (i) users’ social media usage and their psychological attributes and mental health status and (ii) users’ psychological attributes and mental health status and their academic performance. Thirdly, we build a two-level hybrid model to predict academic performance (i.e., Grade Point Average (GPA)) from students’ Facebook posts: (1) from Facebook posts to mental health and psychological attributes using a regression model (SM-MP model) and (2) from psychological and mental attributes to the academic performance using a classifier model (MP-AP model). Later, we conduct an evaluation study by using real-life samples to validate the performance of the model and compare the performance with Baseline Models (i.e., Linguistic Inquiry and Word Count (LIWC) and Empath). Our model shows a strong performance with a microaverage f-score of 0.94 and an AUC-ROC score of 0.95. Finally, we build an ensemble model by combining both the psychological attributes and the mental health models and find that our combined model outperforms the independent models.
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Keyword:
BiLSTM; classification; ensemble; Facebook; MPNet; psychological attributes and mental health; regression; word embedding
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URL: https://doi.org/10.3390/bs12040087
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| 8 |
Vec2Dynamics: A Temporal Word Embedding Approach to Exploring the Dynamics of Scientific Keywords—Machine Learning as a Case Study
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In: Big Data and Cognitive Computing; Volume 6; Issue 1; Pages: 21 (2022)
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| 9 |
Methods, Models and Tools for Improving the Quality of Textual Annotations
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In: Modelling; Volume 3; Issue 2; Pages: 224-242 (2022)
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