Classification of Sentiment Analysis Using Machine Learning

Parikh, Satyen M.; Shah, Mitali K.

doi:10.1007/978-3-030-38040-3_8

Cited by 4 publications

(2 citation statements)

References 22 publications

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“…Most recently, Satyen M. et al utilized Python-based Tweepy solution to extract Twitter content to execute the classification algorithm. The features are extracted and classified -among positive, negative and neutralusing N-gram modelling technique with supervised ML algorithms of SVM and KNN (K-Nearest Neighbor) for classification [9].…”

Section: Related Workmentioning

confidence: 99%

Sentiment Analysis of Text with Lossless Mining

Razaq

Kavianpour

Hales

2021

2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)

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Section: Related Workmentioning

confidence: 99%

Sentiment Analysis of Text with Lossless Mining

Razaq

Kavianpour

Hales

2021

2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)

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“…The sentiments of the users were studied by analysing the Twitter data in [16]. A classification model named SVM is used in the existing framework for classifying input data into seven classes using the SANTA Tool.…”

Section: Related Workmentioning

confidence: 99%

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

Elnadree

El-Sisi

Atwa

2021

IJCI. International Journal of Computers and Information

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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 apply to optimise 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 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, results show the classification technique, Naive Bayes is the lowest performance.

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