A Novel Position-based Sentiment Classification Algorithm for Facebook Comments

With the popularisation of social networks, people are now more at ease to share their thoughts, ideas, opinions and views about all kinds of topics on public platforms. Millions of users are connected each day on social networks and they often contribute to online crimes by their comments or posts...

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Vydané v:International journal of advanced computer science & applications Ročník 7; číslo 10
Hlavní autori: Surroop, Khunishkah, Canoo, Khushboo, Pudaruth, Sameerchand
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: West Yorkshire Science and Information (SAI) Organization Limited 01.01.2016
Predmet:
Algorithms
Bullying
Classification
Crime
Cybercrime
Datasets
Machine learning
Police
Sentiment analysis
Social networks
Theft
Mauritius
ISSN:2158-107X, 2156-5570
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Abstract With the popularisation of social networks, people are now more at ease to share their thoughts, ideas, opinions and views about all kinds of topics on public platforms. Millions of users are connected each day on social networks and they often contribute to online crimes by their comments or posts through cyberbullying, identity theft, online blackmailing, etc. Mauritius has also registered a surge in the number of cybercrime cases during the past decade. In this study, a trilingual dataset of 1031 comments was extracted from public pages on Facebook. This dataset was manually categorised into four different sentiment classes: positive, negative, very negative and neutral, using a novel sentiment classification algorithm. Out of these 1031 comments, it was found that 97.8% of the very negative sentiments, 70.7% of the negative sentiments and 77.0% of the positive sentiments were correctly extracted. Despite the added complexity of our dataset, the accuracy of our system is slightly better than similar works in the field. The accuracy of the lexicon-based approach was also much higher than when we used machine learning techniques. The outcome of this research work can be used by the Mauritius Police Force to track down potential cases of cybercrime on social networks. Decisive actions can then be implemented in time.
AbstractList With the popularisation of social networks, people are now more at ease to share their thoughts, ideas, opinions and views about all kinds of topics on public platforms. Millions of users are connected each day on social networks and they often contribute to online crimes by their comments or posts through cyberbullying, identity theft, online blackmailing, etc. Mauritius has also registered a surge in the number of cybercrime cases during the past decade. In this study, a trilingual dataset of 1031 comments was extracted from public pages on Facebook. This dataset was manually categorised into four different sentiment classes: positive, negative, very negative and neutral, using a novel sentiment classification algorithm. Out of these 1031 comments, it was found that 97.8% of the very negative sentiments, 70.7% of the negative sentiments and 77.0% of the positive sentiments were correctly extracted. Despite the added complexity of our dataset, the accuracy of our system is slightly better than similar works in the field. The accuracy of the lexicon-based approach was also much higher than when we used machine learning techniques. The outcome of this research work can be used by the Mauritius Police Force to track down potential cases of cybercrime on social networks. Decisive actions can then be implemented in time.
Author Canoo, Khushboo
Surroop, Khunishkah
Pudaruth, Sameerchand
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SubjectTerms Algorithms
Bullying
Classification
Crime
Cybercrime
Datasets
Machine learning
Police
Sentiment analysis
Social networks
Theft
Title A Novel Position-based Sentiment Classification Algorithm for Facebook Comments
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