12. Impact of sentiment analysis tools to improve patients’ life in critical diseases
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Dhaval Bhoi
Abstract
The development, approbation, and acceptance of various social media tools and applications have opened new doors of opportunity for gaining crucial insight from unstructured information. Sentiment analysis and opinion mining have become popular in modern years and can be applied in diversified application areas like healthcare informatics, sports, financial sector, politics, tourism, and consumer activities and behavior. In this regard, this chapter presents how sentiment analysis can help for betterment of people suffering from critical diseases. Healthcare-related unstructured tweets relating to being shared on Twitter is becoming crowd-pleasing source of information for healthcare research. Sentiment analysis is becoming metric measurement to find out feelings or opinion of patient suffering from severe diseases. Various tools and methodologies are used, from which color-coded Word Cloud can be formed based on sentiment. Exploring the methods used for sentiment analysis on healthcare research can allow us to get better insight and understanding of human feelings and their psychology and mindset. The study shows various types of tools used in each case and different media sources and examines its impact and improvement in diseases like obesity, diabetes, cardiovascular disease, hypertension, schizophrenia, Alzheimer’s disease, and cancer using sentiment analysis and its impact on one’s life. Sentiment analysis helps in designing strategies to improve patients understanding and behavior.
Abstract
The development, approbation, and acceptance of various social media tools and applications have opened new doors of opportunity for gaining crucial insight from unstructured information. Sentiment analysis and opinion mining have become popular in modern years and can be applied in diversified application areas like healthcare informatics, sports, financial sector, politics, tourism, and consumer activities and behavior. In this regard, this chapter presents how sentiment analysis can help for betterment of people suffering from critical diseases. Healthcare-related unstructured tweets relating to being shared on Twitter is becoming crowd-pleasing source of information for healthcare research. Sentiment analysis is becoming metric measurement to find out feelings or opinion of patient suffering from severe diseases. Various tools and methodologies are used, from which color-coded Word Cloud can be formed based on sentiment. Exploring the methods used for sentiment analysis on healthcare research can allow us to get better insight and understanding of human feelings and their psychology and mindset. The study shows various types of tools used in each case and different media sources and examines its impact and improvement in diseases like obesity, diabetes, cardiovascular disease, hypertension, schizophrenia, Alzheimer’s disease, and cancer using sentiment analysis and its impact on one’s life. Sentiment analysis helps in designing strategies to improve patients understanding and behavior.
Chapters in this book
- Frontmatter I
- Preface VII
- Contents XI
- List of contributors XIII
- 1. A review of bone tissue engineering for the application of artificial intelligence in cellular adhesion prediction 1
- 2. Implementation and classification of machine learning algorithms in healthcare informatics: approaches, challenges, and future scope 21
- 3. Cardiac arrhythmia recognition using Stockwell transform and ABC-optimized twin SVM 35
- 4. Computational intelligence approach to address the language barrier in healthcare 53
- 5. Recent advancement of machine learning and deep learning in the field of healthcare system 77
- 6. Predicting psychological disorders using machine learning 99
- 7. Automatic analysis of cardiovascular diseases using EMD and support vector machines 131
- 8. Machine learning approach for exploring computational intelligence 153
- 9. Classification of various image fusion algorithms and their performance evaluation metrics 179
- 10. Recommender system in healthcare: an overview 199
- 11. Dense CNN approach for medical diagnosis 217
- 12. Impact of sentiment analysis tools to improve patients’ life in critical diseases 239
- 13. A fuzzy entropy-based multilevel image thresholding using neural network optimization algorithm 253
- 14. Machine learning in healthcare 277
- 15. Computational health informatics using evolutionary-based feature selection 309
- Index 329
Chapters in this book
- Frontmatter I
- Preface VII
- Contents XI
- List of contributors XIII
- 1. A review of bone tissue engineering for the application of artificial intelligence in cellular adhesion prediction 1
- 2. Implementation and classification of machine learning algorithms in healthcare informatics: approaches, challenges, and future scope 21
- 3. Cardiac arrhythmia recognition using Stockwell transform and ABC-optimized twin SVM 35
- 4. Computational intelligence approach to address the language barrier in healthcare 53
- 5. Recent advancement of machine learning and deep learning in the field of healthcare system 77
- 6. Predicting psychological disorders using machine learning 99
- 7. Automatic analysis of cardiovascular diseases using EMD and support vector machines 131
- 8. Machine learning approach for exploring computational intelligence 153
- 9. Classification of various image fusion algorithms and their performance evaluation metrics 179
- 10. Recommender system in healthcare: an overview 199
- 11. Dense CNN approach for medical diagnosis 217
- 12. Impact of sentiment analysis tools to improve patients’ life in critical diseases 239
- 13. A fuzzy entropy-based multilevel image thresholding using neural network optimization algorithm 253
- 14. Machine learning in healthcare 277
- 15. Computational health informatics using evolutionary-based feature selection 309
- Index 329
