Sentiment analysis and text categorization of cancer medical records with LSTM

• Published in: Journal of ambient intelligence and humanized computing Vol. 14; no. 5; pp. 5309 - 5325
• Main Authors: Edara, Deepak Chowdary, Vanukuri, Lakshmi Prasanna, Sistla, Venkatramaphanikumar, Kolli, Venkata Krishna Kishore
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2023; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Big Data; Cancer; Classification; Computational Intelligence; Data mining; Datasets; Deep learning; Engineering; Machine learning; Medical research; Neural networks; Original Research; Patients; Potential flow; Robotics and Automation; Sentiment analysis; Social networks; User Interfaces and Human Computer Interaction; Latent Dirichlet Allocation; Long short-term memory; Sentiment analysis; Medical informatics; Cancer; Principal component analysis
• ISSN: 1868-5137; 1868-5145
• DOI: 10.1007/s12652-019-01399-8

Cancer is one among leading diseases, which affects millions of people and families around the world. Monitoring the mood of such cancer affected people plays a vital part in their treatment. In recent days, social media provides a platform for many people to share their experiences about the cancer through various blogs and communities. In this study, we intended to analyse moods of various cancer affected patients by collecting tweets from different online cancer supported communities. We employed several text mining and machine learning strategies to perform sentiment analysis on a distributed framework and developed a model for easier and faster analysis. The proposed distributed framework with long short-term memory (LSTM) neural network is an alternative to the conventional sentiment analysis approaches in analysing large volumes of data in a potential flow. The effectiveness of proposed framework was evaluated on the proposed dataset (corpus-1) and other two benchmark datasets like Health news Tweets (corpus-2) and Medical abstracts (corpus-3). The performance of each text mining and classification method was separately evaluated on three datasets and compared to each other. The results proved that the proposed approach performed better among the other methods in terms of both accuracy and execution time.