Our literature review indicates that despite notable advancements in text mining and NLP, there remains a heavy reliance on supervised methods that struggle with practical application.
Industrial studies primarily adopt simplified methods and often overlook a complete end-to-end process, especially neglecting how to handle data heterogeneity in practical situations.
Noteworthy is the paucity of research focusing on the healthcare domain, which our forthcoming work aims to rectify by addressing existing literature gaps.
Significantly, except for a few specialized areas like the legal domain, the usage of rule-based methods and domain lexicons is prevalent in text mining/NLP studies.
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