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Psychological Well-Being of Left-Behind Children in China: Text Mining of the Social Media Website Zhihu.
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In: International journal of environmental research and public health, vol 19, iss 4 (2022)
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| 2 |
Mining an English-Chinese parallel Dataset of Financial News
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In: Journal of Open Humanities Data; Vol 8 (2022); 9 ; 2059-481X (2022)
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“Thou Shalt Not Take the Lord’s Name in Vain”: A Methodological Proposal to Identify Religious Hate Content on Digital Social Networks
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In: International Journal of Communication; Vol 16 (2022); 22 ; 1932-8036 (2022)
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WS16: Italian heritage: Using corpus data to map phonological patterns in Brazilian Veneto ...
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| 5 |
LASSO Regression Modeling on Prediction of Medical Terms among Seafarers’ Health Documents Using Tidy Text Mining
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In: Bioengineering; Volume 9; Issue 3; Pages: 124 (2022)
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A Corpus-Based Sentence Classifier for Entity–Relationship Modelling
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In: Electronics; Volume 11; Issue 6; Pages: 889 (2022)
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| 7 |
Text Mining from Free Unstructured Text: An Experiment of Time Series Retrieval for Volcano Monitoring
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In: Applied Sciences; Volume 12; Issue 7; Pages: 3503 (2022)
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A Novel Approach for Semantic Extractive Text Summarization
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In: Applied Sciences; Volume 12; Issue 9; Pages: 4479 (2022)
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| 9 |
Using Conceptual Recurrence and Consistency Metrics for Topic Segmentation in Debate
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In: Applied Sciences; Volume 12; Issue 6; Pages: 2952 (2022)
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| 10 |
Predicting the Success of Internet Social Welfare Crowdfunding Based on Text Information
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In: Applied Sciences; Volume 12; Issue 3; Pages: 1572 (2022)
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| 11 |
How We Failed in Context: A Text-Mining Approach to Understanding Hotel Service Failures
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In: Sustainability; Volume 14; Issue 5; Pages: 2675 (2022)
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| 12 |
Psychological Well-Being of Left-Behind Children in China: Text Mining of the Social Media Website Zhihu
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In: International Journal of Environmental Research and Public Health; Volume 19; Issue 4; Pages: 2127 (2022)
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Abstract:
China’s migrant population has significantly contributed to its economic growth; however, the impact on the well-being of left-behind children (LBC) has become a serious public health problem. Text mining is an effective tool for identifying people’s mental state, and is therefore beneficial in exploring the psychological mindset of LBC. Traditional data collection methods, which use questionnaires and standardized scales, are limited by their sample sizes. In this study, we created a computational application to quantitively collect personal narrative texts posted by LBC on Zhihu, which is a Chinese question-and-answer online community website; 1475 personal narrative texts posted by LBC were gathered. We used four types of words, i.e., first-person singular pronouns, negative words, past tense verbs, and death-related words, all of which have been associated with depression and suicidal ideations in the Chinese Linguistic Inquiry Word Count (CLIWC) dictionary. We conducted vocabulary statistics on the personal narrative texts of LBC, and bilateral t-tests, with a control group, to analyze the psychological well-being of LBC. The results showed that the proportion of words related to depression and suicidal ideations in the texts of LBC was significantly higher than in the control group. The differences, with respect to the four word types (i.e., first-person singular pronouns, negative words, past tense verbs, and death-related words), were 5.37, 2.99, 2.65, and 2.00 times, respectively, suggesting that LBC are at a higher risk of depression and suicide than their counterparts. By sorting the texts of LBC, this research also found that child neglect is a main contributing factor to psychological difficulties of LBC. Furthermore, mental health problems and the risk of suicide in vulnerable groups, such as LBC, is a global public health issue, as well as an important research topic in the era of digital public health. Through a linguistic analysis, the results of this study confirmed that the experiences of left-behind children negatively impact their mental health. The present findings suggest that it is vital for the public and nonprofit sectors to establish online suicide prevention and intervention systems to improve the well-being of LBC through digital technology.
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Keyword:
left-behind children; linguistic analysis; personal narrative text; psychological well-being; text mining; textual analysis
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URL: https://doi.org/10.3390/ijerph19042127
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| 13 |
Study of the Yahoo-Yahoo Hash-Tag Tweets Using Sentiment Analysis and Opinion Mining Algorithms
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In: Information; Volume 13; Issue 3; Pages: 152 (2022)
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| 14 |
Preparing Legal Documents for NLP Analysis: Improving the Classification of Text Elements by Using Page Features
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| 17 |
Corona-Rechtsprechung des Bundesverfassungsgerichts (BVerfG-Corona) ...
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[R] Source Code der Corona-Rechtsprechung des Bundesverfassungsgerichts (BVerfG-Corona-Source) ...
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[R] Source Code der Corona-Rechtsprechung des Bundesverfassungsgerichts (BVerfG-Corona-Source) ...
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Corona-Rechtsprechung des Bundesverfassungsgerichts (BVerfG-Corona) ...
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