Open AccessSentiment Analysis Approach for Analyzing iPhone Release using Support Vector Machine
Author(s) -
Wasim Bourequat,
Hassan Mourad
Publication year - 2021
Publication title -
international journal of advances in data and information systems
Language(s) - English
Resource type - Journals
ISSN - 2721-3056
DOI - 10.25008/ijadis.v2i1.1216
Subject(s) - sentiment analysis , lexical analysis , support vector machine , computer science , social media , preprocessor , artificial intelligence , natural language processing , precision and recall , social media analytics , public opinion , text processing , microblogging , process (computing) , text mining , sentence , baseline (sea) , recall , world wide web , politics , linguistics , oceanography , philosophy , political science , law , geology , operating system
Sentiment analysis is a process of understanding, extracting, and processing textual data automatically to get sentiment information contained in a comment sentence on Twitter. Sentiment analysis needs to be done because the use of social media in society is increasing so that it affects the development of public opinion. Therefore, it can be used to analyze public opinion by applying data science, one of which is Natural Language Processing (NLP) and Text Mining or also known as text analytics. The stages of the overall method used in this study are to do text mining on the Twitter site regarding iPhone Release with methods of scraping, labeling, preprocessing (case folding, tokenization, filtering), TF-IDF, and classification of sentiments using the Support Vector Machine. The Support Vector Machine is widely used as a baseline in text-related tasks with satisfactory results, on several evaluation matrices such as accuracy, precision, recall, and F1 score yielding 89.21%, 92.43%, 95.53%, and 93.95, respectively.