Improved Text Classification of Long-term Care Materials (2021)

Abstract: Aging populations have posed a challenge to many countries including Taiwan, and with them come the issue of long-term care. Given the current context, the aim of this study was to explore the hotly-discussed subtopics in the field of long-term care, and identify its features through NLP. This study applied TF-IDF, the Logistic Regression model, and the Naive Bayes classifier to process data. In sum, the results showed that it reached a best F1-score of 0.920 in identification, and a best accuracy of 0.708 in classification. The results of this study could be used as a reference for future long-term care related applications.

## introduction long-term care, by the definition of ministry of health and welfare 1 , refers to "the living support, assistance, social participation, care and relevant healthcare services in accordance with the needs of any individual whose mental or physical incapacity has lasted or is expected to last for six months or longer". as for long-term care services, according to @xcite , include assistance with activities of daily living (abbreviated as adls, which includes activities such as dressing, bathing, and toileting), instrumental activities of daily living (abbreviated as iadls, which includes activities such as medication management and housework), and health maintenance tasks. longterm care services assist people to improve or maintain an optimal level of physical functioning and quality of life, which can include help from other people and special equipment or assistive devices. according to national development council (2020) 2 , driven by a low birth rate of 1.2 and an alltime high life expectancy in 2020, senior citizens (people aged 65 or older) will account for over 20 percent of taiwan's total population by 2025, indicating taiwan will step into a super-aged society. within the context, how long-term care services can meet the escalating demand is absolutely critical. while extensive long-term care studies have been conducted, few of them are from the perspective of caregivers, especially family caregivers. chen (2013) pointed out that long-term care system in taiwan lacked support services for the caregivers. for most family caregivers, they provided adls and iadls for their family members. sometimes, they might get stuck when being confronted with several care problems. @xcite showed that their needs included respite care services, psychological and educational support programs, and financial subsidies. among psychological and educational support programs, caring skills and consulting services were what the caregivers desperately wanted while taking care of their family members. in the course of caring, emergencies could happen. caregivers might need timely help but lack sufficient time to search and browse -for an appropriate answer to their question. besides, caregivers might be in need when it was late at night and had no one to turn to. despite the abundant resources on the internet, not every piece of information is suitable for caregivers, let alone those irrelevant discussions. still, they had to filter. to bridge the gap, this study aimed to explore features useful to identify (1) long-term care and unrelated texts (2) long-term care topics that generate intensive discussion, so that the application could be designed more user-friendly, and caregivers could get the needed information more efficiently. at present, despite the fact that there are many online long-term care platforms, their manner and principles of classification are not based on caregivers' needs. this study intended to fill this gap by collecting authentic materials, adjusting its categories manually and processing them with caregiver-oriented topics. this study sought to answer the following research questions: 1. what are the topics that the caregivers have been hotly discussing? 2. how to provide caregiver-oriented information through nlp? ## literature review in terms of much-discussed topics, according to a global overview study by @xcite , the simultaneous analysis of both references and keywords revealed that common long-term care hot topics included 'dementia care', 'quality of care', 'prevalence and risk factors', 'mortality', and 'randomized controlled trial'. back in taiwan, @xcite had studied about what topics in an online blog would readers (including family caregivers or others) mainly follow. the study suggested that out of the eight categories, the most commonly read and discussed topics were 'family relationships', 'caregiving experiences', 'caregiving stress', 'physical, psychological, and social adaptation', and 'seniors care issues'. the left ones were 'long-term care proj.ndc.gov.tw/upload/download/ (2020 2070 ) .pdf the 33rd conference on computational linguistics and speech processing (rocling 2021) taoyuan, taiwan, october 15-16, 2021. the association for computational linguistics and chinese language processing policies', 'the ups and downs in caring', and 'special caregiver groups' -such as male caregivers and former caregivers. in order to find appropriate features, this paper applied tf-idf, which was consulted from studies by @xcite and paik (2013). they both applied tf-idf weighting in their research. ## methodology the procedure of this research could be presented as figure 3 .1. firstly, data from three platforms were collected and separated into caregiver-oriented ones and non-caregiver-oriented ones. secondly, a task of annotation was done to appropriately classify relevant data into eight categories. thirdly, by appling tf-idf, the logistic regression model, and the naive bayes classifier, features were extracted. afterwards, manually obtained features were also taken into consideration. lastly, with the model, it was hoped to be beneficial to different applications, such as chatbot development or website-building. it could help website designers to build a more caregiver-friendly platform. figure3.1: main procedure of this study