Performance Investigation of Features Extraction and Classification Approaches for Sentiment Analysis Systems
Elnadree, R., El-Sisi, A., atwa, W. (2022). Performance Investigation of Features Extraction and Classification Approaches for Sentiment Analysis Systems. EKB Journal Management System, 9(1), 1-14. doi: 10.21608/ijci.2021.65578.1044
Raghdah Sherif Elnadree; Ashraf Bahgat El-Sisi; walid said atwa. "Performance Investigation of Features Extraction and Classification Approaches for Sentiment Analysis Systems". EKB Journal Management System, 9, 1, 2022, 1-14. doi: 10.21608/ijci.2021.65578.1044
Elnadree, R., El-Sisi, A., atwa, W. (2022). 'Performance Investigation of Features Extraction and Classification Approaches for Sentiment Analysis Systems', EKB Journal Management System, 9(1), pp. 1-14. doi: 10.21608/ijci.2021.65578.1044
Elnadree, R., El-Sisi, A., atwa, W. Performance Investigation of Features Extraction and Classification Approaches for Sentiment Analysis Systems. EKB Journal Management System, 2022; 9(1): 1-14. doi: 10.21608/ijci.2021.65578.1044

Performance Investigation of Features Extraction and Classification Approaches for Sentiment Analysis Systems

IJCI. International Journal of Computers and Information
Article 2, Volume 9, Issue 1, January 2022, Page 1-14  XML PDF (1.59 MB)
Document Type: Original Article
DOI: 10.21608/ijci.2021.65578.1044
View on SCiNiTO View on SCiNiTO
Authors
Raghdah Sherif Elnadree email 1; Ashraf Bahgat El-Sisi2; walid said atwa3
1Computer science department Faculty Of Computer And Information Menoufia university
2Faculty of Computers and Information Menoufia University
3computer sciences department,faculty of computers and information, Menoufia university
Abstract
Data pre-processing and feature extraction of micro-blogging data in sentiment analysis systems becomes an effective field of analysis. Object identification, negation expressions, sarcasm, outlines, misspellings are the major issues faced during sentiment analysis. So, data pre-processing in a sentiment analysis system is a conclusive step to improve data quality, raise the extraction, and classification of meaningful data. This paper presents a sentiment analysis system for performance investigation. Several pre-processing and feature extraction techniques are applied to optimize the sentiment analysis. Our system comprises three different components: data pre-processing, feature extraction, and sentiment analysis. The pre-processing and feature extraction approaches enhance the sentiment analysis system performance. We compare between different sentiment analysis approaches using a dataset of US Airlines from Twitter. Results show achieving high performance when using the Word2Vec approach with XGBoost and random forest classification algorithms. Also, the results show the classification technique, Naive Bayes is the lowest performance.
Keywords
Sentiment Analysis; Classification; Features Extraction; Microblogging; Machine learning
Statistics
Article View: 600
PDF Download: 522