Open AccessBuilding a Sentiment Analysis System Using Automatically Generated Training Dataset
Author(s) -
Daoud Daoud,
Samir Abou El-Seoud
Publication year - 2020
Publication title -
international journal of online and biomedical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.184
H-Index - 8
ISSN - 2626-8493
DOI - 10.3991/ijoe.v16i06.13623
Subject(s) - computer science , sentiment analysis , training set , naive bayes classifier , arabic , set (abstract data type) , artificial intelligence , natural language processing , machine learning , data mining , support vector machine , philosophy , linguistics , programming language
In this paper, we describe a methodology to develop a large training set for sentiment analysis automatically. We extract Arabic tweets and then annotates them for negativeness and positiveness sentiment without human intervention. These annotated tweets are used as a training data set to build our experimental sentiment analysis by using Naive Bayes algorithm and TF-IDF enhancement. The large size of training data for a highly inflected language is necessary to compensate for the sparseness nature of such languages. We present our techniques and explain our experimental system. We use 200 thousand annotated tweets to train our system. The evaluation shows that our sentiment analysis system has high precision and accuracy measures compared to existing ones.