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EFCA: An Extended Formal Concept Analysis Method for Aspect Extraction in Healthcare Informatics | IEEE Conference Publication | IEEE Xplore

EFCA: An Extended Formal Concept Analysis Method for Aspect Extraction in Healthcare Informatics


Abstract:

With the popularity of social media platforms, patients tend to share their experiences and opinions on them, and patient feedback is key to improving health services. Se...Show More

Abstract:

With the popularity of social media platforms, patients tend to share their experiences and opinions on them, and patient feedback is key to improving health services. Sentiment analysis techniques have been applied to automatically analyze the patients’ opinions to understand the quality of healthcare. Aspect extraction, which aims to identify the opinion targets in the text, is an important step towards understanding the patient’s opinion towards particular target or entity. However, due to the complex nature of medical domain data, existing approaches take much execution time. To address this, we presents a new approach for aspect extraction and refinement to smooth the sentiment analysis process. Furthermore, this work also introduces an intelligent weighting scheme for classifying the final aspects. For the experimental evaluation, a dataset from Yelp and RateMDs has been utilized. Experimental results show that the proposed model outperforms existing methods.
Date of Conference: 09-12 December 2021
Date Added to IEEE Xplore: 14 January 2022
ISBN Information:
Conference Location: Houston, TX, USA

Funding Agency:


References

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