A Review of Feature Selection Algorithms in Sentiment Analysis for Drug Reviews

Abstract

Social media data contain various sources of big data that include data on drugs, diagnosis, treatments, diseases, and indications. Sentiment analysis (SA) is a technology that analyses text-based data using machine learning techniques and Natural Language Processing to interpret and classify emotions in the subjective language. Data sources in the medical domain may exist in the form of clinical documents, nurse’s letter, drug reviews, MedBlogs, and Slashdot interviews. It is important to analyse and evaluate these types of data sources to identify positive or negative values that could ensure the well-being of the users or patients being treated. Sentiment analysis technology can be used in the medical domain to help identify either positive or negative issues. This approach helps to improve the quality of health services offered to consumers. This paper will be reviewing feature selection algorithms, sentiment classifications, and standard measurements that are used to measure the performance of these techniques in previous studies. The combination of feature extraction techniques based on Natural Language Processing with Machine Learning techniques as a feature selection technique can reduce the size of features, while selecting relevant features can improve the performance of sentiment classifications. This study will also describe the use of metaheuristic algorithms as a feature selection algorithm in sentiment analysis that can help achieve higher accuracy for optimal subset selection tasks. This review paper has also identified previous studies that applied metaheuristics algorithm as a feature selection algorithm in the medical domain, especially studies that used drug review data.