Tesis Sopian Aji

Tesis Sopian Aji

• By Aji, Sopian;
• Disertasi
• 281

Sentiment analysis is process that contains text-based datasets which are positive, negative or neutral. Social media has provided a place for web users to express sharing of thoughts, opinions and convey news on different topics in an event. Haters in various media, including social media, can be punished. In the circular, stated that hate speech issue has been getting attention both national and international community concern for the protection of human rights (HAM). Classification of algorithms such as Naive Bayes (NB) and Particle Swarm Optimization (PSO) was proposed by many researchers to be used in the analysis of text sentiment. Naive Bayes’s algorithms and methods, will be tested with two inputs using tokenize and Transform Cases’s comments are positive (100 text comments) and negative (100 comments text), it obtained experimental results accuracy: 62.50 % +/- 7.50 % (micro : 62.50 %).  The result will be increased if the results of the experiment combine with Particle Swarm Optimization (PSO), it obtained experimental results were better accuracy: +/- 74.00 % 7:68 % (micro : 74.00 %). The results showed that Naive Bayes (NB) will get the best results if it combined with Particle Swarm Optimization (PSO). 

 

Keywords : sentiment analysis, internet ethics, social media, Naive Bayes, particle swarm optimization

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