KLASIFIKASI ALGORITMA TF DAN NEUTRAL NETWORK DALAM SENTIMEN ANALISIS

Authors

  • Amril Mutoi Siregar Universitas Buana Perjuangan Karawang

DOI:

https://doi.org/10.32627/aims.v1i2.359

Keywords:

algoritma term frequency, Neural Network, Sentimen, Twitter, text mining

Abstract

Nowadays social media has become one of the tools to express idea or opinion. They are more active expressing it on social media instead of speaking directly. Twitter is the most popular among them to express idea, also share news, picture, music and etc. Twitter users are increasing significantly each year as the result the information grows in same way. Due too much information flow, people get difficulties to make sure or clarify the news. For example, Looking for the information about a figure who will participate in a Pilkada. There are many researchers analyze subjectively and haven’t given the maximum result yet. This research is trying to clarify information and divided them into positive, negative and neutral information. It is using TF algorithm and Neutral Network as the tools. The dataset is taken from a figure’ twitter which is participate in Pilkada. And the result shows that accuracy 66.92%, positive precision 67.80%, negative precision  64.29%, neutral precision 73.33%, and positive recall 80%, negative recall 70%, neutral recall 36.67%.

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Published

2018-12-12

Issue

Section

Articles