Dual-Language Sentiment Analysis: A Comprehensive Evaluating SVM, Logistic Regression, XGBoost, and Decision Tree Using TF-IDF On Arabic and English Dataset

Hawraa Ali Taher

doi:10.31185/wjps.549

Authors

Hawraa Ali Taher Department of Computer Science, Faculty of Education for Girls, University of Kufa, IRAQ

DOI:

https://doi.org/10.31185/wjps.549

Keywords:

Sentiment Analysis, TF-IDF, Arabic Language, English Language, Machine Learning Algorithms

Abstract

Sentiment analysis (SA) is a growing area of study that straddles a number of disciplines, including machine learning, data mining, and natural language processing. It is focused on the automatic extraction of viewpoints presented in a certain text. Many studies have been conducted in the area of sentiment analysis because to its broad uses, particularly on texts in English, whereas other languages like Arabic have gotten less attention. The Arabic language presents several difficulties, such as its rich morphology and the difficulty of tracing words back to their original roots. Arabic comments have been analyzed and categorized into good and negative attitudes using a framework. With the aim of evaluating any tweet, opinion, purpose, or reputation, such as a university, company, mobile, and others, the research analyzes the comments made by users of the social networking site Twitter. It does this by using classification technology and machine learning, which are among the fundamental tasks of the data mining process used in the larger process, which is to explore knowledge . This search helps the user to access the evaluation of other users through their tweets and comments on the social networking site for an opinion immediately and automatically, and then the process of uploading and evaluating the opinions using appropriate algorithms for this purpose as(Decision Tree classifier DTC , XGboost, Logistic Regression LR, Support Vector Machine SVM )with Term Frequency-Inverse Document Frequency TF_IDF

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