research-article
Author: Megha Sharma
Published: 13 May 2024 Publication History
- 0citation
- 8
- Downloads
Metrics
Total Citations0Total Downloads8Last 12 Months8
Last 6 weeks6
New Citation Alert added!
This alert has been successfully added and will be sent to:
You will be notified whenever a record that you have chosen has been cited.
To manage your alert preferences, click on the button below.
Manage my Alerts
New Citation Alert!
Please log in to your account
Get Access
- Get Access
- References
- Media
- Tables
- Share
Abstract
Now Health care industry grows so much and patient share their experience and those reviews help other to enhance and improving their knowledge. The trend of sharing thoughts, ideas, opinions, reviews, ratings, etc. on social media is growing, which gave lot of unstructured data. For these types of unstructured data supervised learning methods are good to extract something and improving the performance of the machine. NLP is the best method to extract information from the text data. Not only NLP but Machine Learning algorithm with NLP methods gave which is high accurate to train our machine for better prediction. In this paper UCI ML drug review dataset used from kaggle website and this dataset provide patients reviews on specified drugs along with some conditions. Bag of words and TF-IDF model along with Naïve bayes and Passive aggressive classifier algorithm to train and test the machine to classify the patients review text data. Our objective is to find which NLP model and ML algorithm is good based on the accuracy and is our machine able to classify patient review and predict condition based on the review.
References
[1]
Hanauer D, Zheng K, Singer D, Gebremariam A, Davis M. PUblic awareness, perception, and use of online physician rating sites. JAMA. 2014 Feb.
[2]
PwC Scoring healthcare: Navigating customer experience ratings. [2014-03-07].
[3]
http://www.pwc.com/us/en/health- industries/publications/scoring-patient-healthcare- experience.jhtml .
[4]
Shoemaker P. What value-based purchasing means to your hospital. Healthc Financ Manage. 2011 Aug;65(8):60– 8.
[5]
Ikonomakis, M., S. Kotsiantis, and V. Tampakas. "Text classification: a recent overview." DNCOCO’10. Proceedings of the 9th WSEAS International Conference on Data Networks, Communications, Computers. 2005.
[6]
https://www.kaggle.com/datasets/jessicali9530/kuc- hackathon-winter-2018
[7]
https://www.ibm.com/in-en/topics/exploratory-data- analysis
[8]
https://www.betterevaluation.org/methods- approaches/methods/word-cloud.
[9]
Gowrinath, C., C. Hari, and P. Reddy. "Evaluating Frequency of words and Word Cloud from Astrological sentiments using NLP." International Journal of Scientific Research in Science and Technology 11 (2021): 920-928.
[10]
Kannan, Subbu, "Preprocessing techniques for text mining." International Journal of Computer Science & Communication Networks 5.1 (2014): 7-16.
[11]
https://www.mygreatlearning.com/blog/bag-of-words/
[12]
https://www.javatpoint.com/machine-learning-naive- bayes-classifier
[13]
V. Agarwal, H. P. Sultana, S. Malhotra, and A. Sarkar, "Analysis of Classifiers for Fake News
[14]
Detection," Procedia Comput. Sci., vol. 165, no. 2019, pp. 377–383, 2019.
Digital Library
[15]
S. Deepak and B. Chitturi, "Deep neural approach to Fake-News identification," Procedia Comput. Sci., vol. 167, no. 2019, pp. 2236– 2243, 2020.
[16]
S. A. Cammel, "How to automatically turn patient experience free-text responses into actionable insights: A natural language programming (NLP) approach," BMC Med. Inform. Decis. Mak., vol. 20, no. 1, pp. 1–10, 2020. 1104-5
[17]
M. U. Salur and I. Aydin, "A Novel Hybrid Deep Learning Model for Sentiment Classification," IEEE Access, vol. 8, pp. 58080–58093, 2020.
[18]
C. Zhang, A. Gupta, C. Kauten, A. V. Deokar, and X. Qin, "Detecting fake news for reducing misinformation risks using analytics approaches," Eur. J. Oper. Res., vol. 279, no. 3, pp. 1036–1052, 2019.
[19]
W. Haitao, H. Jie, Z. Xiaohong, and L. Shufen, "A short text classification method based on n-gram and cnn," Chinese J. Electron., vol. 29, no. 2, pp. 248– 254, 2020.
[20]
Z. Li, F. Yang, and Y. Luo, "Context Embedding Based on Bi-LSTM in Semi-Supervised Biomedical Word Sense Disambiguation," IEEE Access, vol. 7, pp. 72928–72935, 2019.
[21]
K. Liu and L. Chen, "Medical Social Media Text Classification Integrating Consumer Health Terminology," in IEEE Access, vol. 7, pp. 78185-78193, 2019. Singh, U. P., Saxena, V., Kumar, A., Bhari, P., & Saxena, D. (2022, December). Unraveling the Prediction of Fine Particulate Matter over Jaipur, India using Long Short-Term Memory Neural Network. In Proceedings of the 4th International Conference on Information Management & Machine Intelligence (pp. 1-5).
[22]
Kumar, A., Bhari, P. L., Singh, U. P., & Saxena, V. (2022, December). Comparative Study of different Machine Learning Algorithms to Analyze Sentiments with a Case Study of Two Person's Microblogs on Twitter. In Proceedings of the 4th International Conference on Information Management & Machine Intelligence (pp. 1-6).
Digital Library
[23]
Saxena, V., Saxena, D., & Singh, U. P. (2022, December). Security Enhancement using Image verification method to Secure Docker Containers. In Proceedings of the 4th International Conference on Information Management & Machine Intelligence (pp. 1-5).
Digital Library
[24]
Chauhan, M., Malhotra, R., Pathak, M., & Singh, U. P. (2012). Different aspects of cloud security. International Journal of Engineering Research and Applications, 2, 864-869.
[25]
Mittal, A. K., Singh, U. P., Tiwari, A., Dwivedi, S., Joshi, M. K., & Tripathi, K. C. (2015). Short-term predictions by statistical methods in regions of varying dynamical error growth in a chaotic system. Meteorology and Atmospheric Physics, 127, 457-465.
[26]
Singh, U. P., Mittal, A. K., Dwivedi, S., & Tiwari, A. (2015). Predictability study of forced Lorenz model: an artificial neural network approach. History, 40(181), 27-33.
[27]
Singh, U. P., Mittal, A. K., Dwivedi, S., & Tiwari, A. (2020). Evaluating the predictability of central Indian rainfall on short and long timescales using theory of nonlinear dynamics. Journal of water and Climate Change, 11(4), 1134-1149.
[28]
Singh, U., Pathak, M., Malhotra, R., & Chauhan, M. (2012). Secure communication protocol for ATM using TLS handshake. Journal of Engineering Research and Applications (IJERA), 2(2), 838-948.
[29]
Singh, U. P., & Mittal, A. K. (2021). Testing reliability of the spatial Hurst exponent method for detecting a change point. Journal of Water and Climate Change, 12(8), 3661-3674.
[30]
Tiwari, A., Mittal, A. K., Dwivedi, S., & Singh, U. P. (2015). Nonlinear time series analysis of rainfall over central Indian region using CMIP5 based climate model. Climate Change, 1(4), 411-417.
[31]
https://www.learndatasci.com/glossary/tf-idf-term- frequency-inverse-document-frequency/
[32]
Berrar, Daniel. "Bayes’ theorem and naive Bayes classifier." Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics 403 (2018): 412.
Recommendations
- Bengali paper classification using ensemble machine learning algorithms
Text classification is one of the most challenging problems in natural language processing (NLP). Language models are at the heart of NLP. The ability to represent texts as numbers has given rise to many NLP tasks, for example, text categorisation, ...
Read More
- Comparing image classification methods: K-nearest-neighbor and support-vector-machines
AMERICAN-MATH'12/CEA'12: Proceedings of the 6th WSEAS international conference on Computer Engineering and Applications, and Proceedings of the 2012 American conference on Applied Mathematics
In order for a robot or a computer to perform tasks, it must recognize what it is looking at. Given an image a computer must be able to classify what the image represents. While this is a fairly simple task for humans, it is not an easy task for ...
Read More
- Urdu text classification
FIT '09: Proceedings of the 7th International Conference on Frontiers of Information Technology
This paper compares statistical techniques for text classification using Naïve Bayes and Support Vector Machines, in context of Urdu language. A large corpus is used for training and testing purpose of the classifiers. However, those classifiers cannot ...
Read More
Comments
Information & Contributors
Information
Published In
ICIMMI '23: Proceedings of the 5th International Conference on Information Management & Machine Intelligence
November 2023
1215 pages
ISBN:9798400709418
DOI:10.1145/3647444
- Editors:
- Dinesh Goyal,
- Anil Kumar,
- Dharm Singh,
- Marcin Paprzycki,
- Pooja Jain,
- B. B. Gupta,
- Uday Pratap Singh
Copyright © 2023 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [emailprotected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 13 May 2024
Permissions
Request permissions for this article.
Check for updates
Author Tags
- Bag of words model
- Keywords— Text classification
- ML
- NLP
- Naïve bayes algorithm
- Passive aggressive classifier
- TF-IDF model
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
ICIMMI 2023
ICIMMI 2023: International Conference on Information Management & Machine Intelligence
November 23 - 25, 2023
Jaipur, India
Contributors
Other Metrics
View Article Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
Total Citations
8
Total Downloads
- Downloads (Last 12 months)8
- Downloads (Last 6 weeks)6
Other Metrics
View Author Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in
Full Access
Get this Publication
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML FormatMedia
Figures
Other
Tables