期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2020
卷号：11
期号：3
DOI：10.14569/IJACSA.2020.0110353
出版社：Science and Information Society (SAI)
摘要：Social media is the venue where the opinions are shared in form of text, images and videos by public. Hospitals’ performance can be judged by opinions that are written by patients or their relatives. Machine learning techniques can be used to detect sentiments of the opinion givers. For the research work presented in this article, opinions for few big hospitals were collected using Facebook, twitter and hospitals’ webpage. The corpus was constructed and the sentiment analysis was performed after few preprocessing tasks. Resources like Stanford POS Tagger and WordNet were used to discover aspects. In this paper, the challenges of annotation of subjective opinions are discussed in detail. Two sentiment lexicons namely NRC-Affect-Intensity lexicon and SentiWordNet 3.0 lexicon were used to calculate sentiment scores of the comments that were used by different machine learning classifiers. Moreover, the results of the experiments on the constructed dataset are provided. For the experiments that aimed to discover overall sentiments of user towards hospital, Random forest outperformed other classifiers achieving accuracy of 76.49% using scores from NRC-Affect-Intensity lexicon. For the experiments that were directed towards discovering sentiments of users towards particular aspect of a hospital, Random forest overtook other classifiers reaching accuracy of 80.7339 % using NRC-Affect-Intensity lexicon sentiment scores. The research results show that machine learning can be very helpful in identifying sentiments of users from their textual comments that are vastly available on different social media platforms. The results can be helpful in improvement of hospital performance and are expected to contribute to growing field of health informatics.
关键词：Health informatics; Classification Algorithms; Sentiment Analysis; Sentiment Lexicons; Text Mining
