Skripsi

KLASIFIKASI EMOSI PADA TWITTER DENGAN METODE EMOTION LEXICON DAN NAIVE BAYES

---

Penilaian

---

Social media is a means of interaction and communication. One of the social media that is often used is Twitter. Twitter allows its users to express many things, one of which is being a personal media to provide various kinds of expressions from its users such as emotions. Users can express their emotions and sentiments through writing on the status of their social media posts. One method to find out the emotion of a sentence is the Emotion Lexicon. However, the lexicon based method is not good at classifying data because not every word contains emotion, so it cannot be used as a specific result and a better classification method is needed, namely combining Naïve Bayes with Emotion Lexicon. Naïve Bayes relies on independent assumptions to obtain a classification through the probability hypothesis that each class has. The results of the classification test with Emotion Lexicon alone have 46% accuracy, 45% precision, 51% recall and 36% f-measure. While the results of the classification test with Emotion Lexicon and Naïve Bayes resulted in an accuracy of 65%, precision of 77%, recall of 55%, and f-measure of 59%

---

Availability

Inventory Code	Barcode	Call Number	Location	Status
T508202021	T50820	T508202021	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

-

Call Number

T508202021

Publisher

Inderalaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2021

Collation

xviii, 65 hlm,: ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

-

Classification

006.754 07

Content Type

Text

Media Type

-

Carrier Type

-

Edition

-

Subject(s)

Prodi Teknik Informatika
Situs Jejaring Sosial, Sosial Media, Twitter

Specific Detail Info

-

Statement of Responsibility

MURZ

Other version/related

File Attachment

Comments

---

You must be logged in to post a comment