A Novel Position-based Sentiment Classification Algorithm for Facebook Comments

Abstract

Abstract-With the popularization 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 cyber bullying, 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 categorized 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.

A Combination of Lexicon and Machine Learning Approaches for Sentiment Analysis on Facebook

“…The experiment starts with the first step of extracting UUM Facebook comments. The comments were extracted via the Facepager (Surroop, Canoo, and Pudaruth 2016) tool. Facepager is a scraper tool to extract data that is openly available on Twitter and Facebook.…”

Penggunaan Metode Naïve Bayes Classifier pada Analisis Sentimen Facebook Berbahasa Indonesia

“…Media sosial tersebut diantaranya adalah Facebook, Twitter, Path, Instagram dan media sosial lainnya. Analisis sentimen penting dilakukan karena dengan itu kita dapat mengetahui dengan cepat sentimen pada opini khususnya pada media sosial Facebook [4].…”