Sentiment Analysis in Social Media Based on English Language Multilingual Processing Using Three Different Analysis Techniques

Sentiment Analysis in Social Media Based on English Language Multilingual Processing Using Three Different Analysis Techniques

Numerous numbers of companies have utilized the web to offer their services and products. Web customers dependably look through the reviews of other customers towards a product or service before they chose to buy the things or viewed the films. The company needs to analyse their customers’ sentiment...

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Bibliographic Details
Main Authors: Nor Saradatul Akmar, Zulkifli, Lee, Allen Wei Kiat
Format: Conference or Workshop Item
Language:English
Published: Springer, Singapore 2019
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://umpir.ump.edu.my/id/eprint/25902/
http://umpir.ump.edu.my/id/eprint/25902/
http://umpir.ump.edu.my/id/eprint/25902/
http://umpir.ump.edu.my/id/eprint/25902/1/Sentiment%20Analysis%20in%20Social%20Media%20Based%20on%20English%20Language1.pdf
id ump-25902
recordtype eprints
spelling ump-259022019-10-03T03:57:24Z http://umpir.ump.edu.my/id/eprint/25902/ Sentiment Analysis in Social Media Based on English Language Multilingual Processing Using Three Different Analysis Techniques Nor Saradatul Akmar, Zulkifli Lee, Allen Wei Kiat QA75 Electronic computers. Computer science Numerous numbers of companies have utilized the web to offer their services and products. Web customers dependably look through the reviews of other customers towards a product or service before they chose to buy the things or viewed the films. The company needs to analyse their customers’ sentiment and feeling based on their comments. The outcome of the sentiment analysis makes the companies easily discover either the expression of their users is more to positive or negative. There are numerous numbers of sentiment analysis techniques available in the market today. However, only three (3) techniques will be used in this research which are the Python NLTK Text Classification, Miopia and MeaningCloud. These techniques used to analyse the sentiment analysis of the reviews and comments from English language in social media. 2400 datasets from Amazon, Kaggle, IMdB, and Yelp were used to analyse the accuracy of these techniques. From this analyses, average accuracy for sentiment analysis using Python NLTK Text Classification is 74.5%, meanwhile only 73% accuracy achieved using Miopia technique. The accuracy achieved when using MeaningCloud technique is 82.1% which is the highest compared to other techniques. This shows that hybrid technique offers a greatest accuracy for sentiment analysis on social reviews. Springer, Singapore 2019-08-29 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/25902/1/Sentiment%20Analysis%20in%20Social%20Media%20Based%20on%20English%20Language1.pdf Nor Saradatul Akmar, Zulkifli and Lee, Allen Wei Kiat (2019) Sentiment Analysis in Social Media Based on English Language Multilingual Processing Using Three Different Analysis Techniques. In: Soft Computing in Data Science 5th International Conference on Soft Computing in Data Science, SCDS 2019, 28-29 August 2019 , Iizuka, Japan. pp. 375-385., 1100. ISBN 978-981-15-0399-3 https://doi.org/10.1007/978-981-15-0399-3 https://doi.org/10.1007/978-981-15-0399-3
repository_type Digital Repository
institution_category Local University
institution Universiti Malaysia Pahang
building UMP Institutional Repository
collection Online Access
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Nor Saradatul Akmar, Zulkifli
Lee, Allen Wei Kiat
Sentiment Analysis in Social Media Based on English Language Multilingual Processing Using Three Different Analysis Techniques
description Numerous numbers of companies have utilized the web to offer their services and products. Web customers dependably look through the reviews of other customers towards a product or service before they chose to buy the things or viewed the films. The company needs to analyse their customers’ sentiment and feeling based on their comments. The outcome of the sentiment analysis makes the companies easily discover either the expression of their users is more to positive or negative. There are numerous numbers of sentiment analysis techniques available in the market today. However, only three (3) techniques will be used in this research which are the Python NLTK Text Classification, Miopia and MeaningCloud. These techniques used to analyse the sentiment analysis of the reviews and comments from English language in social media. 2400 datasets from Amazon, Kaggle, IMdB, and Yelp were used to analyse the accuracy of these techniques. From this analyses, average accuracy for sentiment analysis using Python NLTK Text Classification is 74.5%, meanwhile only 73% accuracy achieved using Miopia technique. The accuracy achieved when using MeaningCloud technique is 82.1% which is the highest compared to other techniques. This shows that hybrid technique offers a greatest accuracy for sentiment analysis on social reviews.
format Conference or Workshop Item
author Nor Saradatul Akmar, Zulkifli
Lee, Allen Wei Kiat
author_facet Nor Saradatul Akmar, Zulkifli
Lee, Allen Wei Kiat
author_sort Nor Saradatul Akmar, Zulkifli
title Sentiment Analysis in Social Media Based on English Language Multilingual Processing Using Three Different Analysis Techniques
title_short Sentiment Analysis in Social Media Based on English Language Multilingual Processing Using Three Different Analysis Techniques
title_full Sentiment Analysis in Social Media Based on English Language Multilingual Processing Using Three Different Analysis Techniques
title_fullStr Sentiment Analysis in Social Media Based on English Language Multilingual Processing Using Three Different Analysis Techniques
title_full_unstemmed Sentiment Analysis in Social Media Based on English Language Multilingual Processing Using Three Different Analysis Techniques
title_sort sentiment analysis in social media based on english language multilingual processing using three different analysis techniques
publisher Springer, Singapore
publishDate 2019
url http://umpir.ump.edu.my/id/eprint/25902/
http://umpir.ump.edu.my/id/eprint/25902/
http://umpir.ump.edu.my/id/eprint/25902/
http://umpir.ump.edu.my/id/eprint/25902/1/Sentiment%20Analysis%20in%20Social%20Media%20Based%20on%20English%20Language1.pdf
first_indexed 2023-09-18T22:40:01Z
last_indexed 2023-09-18T22:40:01Z
_version_ 1777416851984220160

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