Sentiment analysis in social media based on English language
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...
| Main Authors: | , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
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Universiti Malaysia Pahang
2019
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/27564/ http://umpir.ump.edu.my/id/eprint/27564/1/117.%20Sentiment%20analysis%20in%20social%20media%20based%20on%20english%20language.pdf http://umpir.ump.edu.my/id/eprint/27564/2/117.1%20Sentiment%20analysis%20in%20social%20media%20based%20on%20english%20language.pdf |
| _version_ | 1848822828874334208 |
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| author | Nor Saradatul Akmar, Zulkifli Lee Wei, Wei Kiat |
| author_facet | Nor Saradatul Akmar, Zulkifli Lee Wei, Wei Kiat |
| author_sort | Nor Saradatul Akmar, Zulkifli |
| building | UMP Institutional Repository |
| collection | Online Access |
| 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 |
| first_indexed | 2025-11-15T02:47:27Z |
| format | Conference or Workshop Item |
| id | ump-27564 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T02:47:27Z |
| publishDate | 2019 |
| publisher | Universiti Malaysia Pahang |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-275642020-04-02T02:38:06Z http://umpir.ump.edu.my/id/eprint/27564/ Sentiment analysis in social media based on English language Nor Saradatul Akmar, Zulkifli Lee Wei, Wei Kiat QA76 Computer software 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 Universiti Malaysia Pahang 2019 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/27564/1/117.%20Sentiment%20analysis%20in%20social%20media%20based%20on%20english%20language.pdf pdf en http://umpir.ump.edu.my/id/eprint/27564/2/117.1%20Sentiment%20analysis%20in%20social%20media%20based%20on%20english%20language.pdf Nor Saradatul Akmar, Zulkifli and Lee Wei, Wei Kiat (2019) Sentiment analysis in social media based on English language. In: International Conference on Mechanical Engineering Research (ICMER 2013) , 1-3 July 2013 , Bukit Gambang Resort, Kuantan, Pahang, Malaysia. pp. 1-12.. (Unpublished) (Unpublished) |
| spellingShingle | QA76 Computer software Nor Saradatul Akmar, Zulkifli Lee Wei, Wei Kiat Sentiment analysis in social media based on English language |
| title | Sentiment analysis in social media based on English language |
| title_full | Sentiment analysis in social media based on English language |
| title_fullStr | Sentiment analysis in social media based on English language |
| title_full_unstemmed | Sentiment analysis in social media based on English language |
| title_short | Sentiment analysis in social media based on English language |
| title_sort | sentiment analysis in social media based on english language |
| topic | QA76 Computer software |
| url | http://umpir.ump.edu.my/id/eprint/27564/ http://umpir.ump.edu.my/id/eprint/27564/1/117.%20Sentiment%20analysis%20in%20social%20media%20based%20on%20english%20language.pdf http://umpir.ump.edu.my/id/eprint/27564/2/117.1%20Sentiment%20analysis%20in%20social%20media%20based%20on%20english%20language.pdf |