Aspect-Based Sentiment Analysis Using Attribute Extraction of Hospital Reviews

被引:11
作者
Bansal, Ankita [1 ]
Kumar, Niranjan [1 ]
机构
[1] Netaji Subhas Univ Technol, Dept Informat & Technol, New Delhi, India
关键词
Aspect-based sentiment analysis; Sentiment analysis; Natural language processing; Web scrapping; SentiWordNet; CLASSIFICATION;
D O I
10.1007/s00354-021-00141-3
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
The Covid pandemic has become a serious public health challenge for people across India and other nations. Nowadays, people rely on the online reviews being shared on different review sites to gather information about hospitals like the availability of beds, availability of ventilators, etc. However, since these reviews are large in number and are unstructured, patients struggle to get accurate and reliable information about the hospitals, due to which they end up taking admission into a hospital which might not be appropriate for the specific treatment they require. This paper employs the use of sentiment analysis to understand various online reviews of hospitals and provide valuable information to the patients. Approximately 30,000 + reviews were collected from more than 500 hospitals. The broad objective of the study is to give the patients a comprehensive and descriptive rating of the hospitals based on the online reviews given by different patients. In addition to providing a comprehensive summary, the study has conducted aspect-based analysis where it compares the hospitals based on four different aspects of the hospital viz. "Doctors' services", "Staff's services", "Hospital facilities", and "Affordability". The database containing aspect-based ratings of the hospitals will be of great value to the patients by allowing them to compare and select the best hospital based on the optimum fit of the aspects of their preference.
引用
收藏
页码:941 / 960
页数:20
相关论文
共 36 条
[1]  
Agarwal Apoorv., 2009, Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics, EACL '09, P24, DOI [10.3115/1609067.1609069, DOI 10.3115/1609067.1609069]
[2]   Semi-supervised Aspect Based Sentiment Analysis for Movies using Review Filtering [J].
Anand, Deepa ;
Naorem, Deepan .
PROCEEDING OF THE SEVENTH INTERNATIONAL CONFERENCE ON INTELLIGENT HUMAN COMPUTER INTERACTION (IHCI 2015), 2016, 84 :86-93
[3]  
[Anonymous], 2004, P 42 ANN M ASS COMPU
[4]  
[Anonymous], 2010, P 19 ACM INT C INFOR, DOI [DOI 10.1145/1871437.1871741, 10.1145/1871437.1871741]
[5]  
[Anonymous], 2004, P 20 INT C COMP LING, DOI DOI 10.3115/1220355.1220555
[6]   Deep Learning for Hate Speech Detection in Tweets [J].
Badjatiya, Pinkesh ;
Gupta, Shashank ;
Gupta, Manish ;
Varma, Vasudeva .
WWW'17 COMPANION: PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, 2017, :759-760
[7]  
Bhatia P., 2020, ADV MATH-SCIEN J, V9, P1857
[8]   Sentiment Analysis of Twitter Data [J].
El Rahman, Sahar A. ;
AlOtaibi, Feddah Alhumaidi ;
AlShehri, Wejdan Abdullah .
2019 INTERNATIONAL CONFERENCE ON COMPUTER AND INFORMATION SCIENCES (ICCIS), 2019, :336-339
[9]  
Go A, 2009, CS224N project report, Stanford, V1
[10]   Use of Sentiment Analysis for Capturing Patient Experience From Free-Text Comments Posted Online [J].
Greaves, Felix ;
Ramirez-Cano, Daniel ;
Millett, Christopher ;
Darzi, Ara ;
Donaldson, Liam .
JOURNAL OF MEDICAL INTERNET RESEARCH, 2013, 15 (11)