Extreme Learning Machine Algorithm in Sentiment Analysis and Its Applications: Systematic Literature Review

Abstract

Natural language processing and machine learning are used to define and extract human emotions from unstructured text using a technique called sentiment analysis. Many organizations and companies today want to use this to recognize and act accordingly on the customer or user's features. This increases the importance and effectiveness of emotion analysis and the diversity of algorithms used day by day. One of these algorithms is the Extreme Learning machine. The Extreme Learning machine (ELM) algorithm is an important machine learning algorithm for emotion analysis and classification. In this study, the method used in the ELM's emotional analysis is systematic research that shows that the context and its applications have been studied. A systematic review of the works published between 2020 and 2022 was carried out using Web of Science and Google Scholar databases. After the first and in-depth screening of the literature, 10 of the 28 articles were selected from the review process. The articles have been reviewed based on the purpose of the study and research questions. According to the research results, different methods were used in the emotional analysis, mostly with the ELM, and ELM’s performance was improved. Quality analysis of treatment summaries is used in different areas, such as health care, education, and website product assessments. ELM's use of emotion analysis has resulted in most social media data as a scope, especially the Twitter platform.