IAES International Journal of Artificial Intelligence (IJ-AI)
Vol 11, No 3: September 2022

The feature extraction for classifying words on social media with the Naïve Bayes algorithm

Arif Ridho Lubis (Universitas Sumatera Utara)
Mahyuddin Khairuddin Matyuso Nasution (Universitas Sumatera Utara)
Opim Salim Sitompul (Universitas Sumatera Utara)
Elviawaty Muisa Zamzami (Universitas Sumatera Utara)

Article Info

Publish Date
01 Sep 2022


To classify Naïve Bayes classification (NBC), however, it is necessary to have a previous pre-processing and feature extraction. Generally, pre-processing eliminates unnecessary words while feature extraction processes these words. This paper focuses on feature extraction in which calculations and searches are used by applying word2vec while in frequency using term frequency-Inverse document frequency (TF-IDF). The process of classifying words on Twitter with 1734 tweets which are defined as a document to weight the calculation of frequency with TF-IDF with words that often come out in tweet, the value of TF-IDF decreases and vice versa. Following the achievement of the weight value of the word in the tweet, the classification is carried out using Naïve Bayes with 1734 test data, yielding an accuracy of 88.8% in the Slack word category tweet and while in the tweet category of verb 78.79%. It can be concluded that the data in the form of words available on twitter can be classified and those that refer to slack words and verbs with a fairly good level of accuracy. so that it manifests from the habit of twitter social media user.

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Journal Info





Computer Science & IT Engineering


IAES International Journal of Artificial Intelligence (IJ-AI) publishes articles in the field of artificial intelligence (AI). The scope covers all artificial intelligence area and its application in the following topics: neural networks; fuzzy logic; simulated biological evolution algorithms (like ...