Eliminasi Non-Topic Menggunakan Pemodelan Topik untuk Peringkasan Otomatis Data Tweet dengan Konteks Covid-19

Penulis

Putri Damayanti, Diana Purwitasari, Nanik Suciati

Abstrak

Akun twitter, seperti Suara Surabaya, dapat membantu menyebarkan informasi tentang COVID-19 meskipun ada bahasan lainnya seperti kecelakaan, kemacetan atau topik lain. Peringkasan teks dapat diimplementasikan pada kasus pembacaan data twitter karena banyaknya jumlah tweet yang tersedia, sehingga akan mempermudah dalam memperoleh informasi penting terkini terkait COVID-19. Jumlah variasi bahasan pada teks tweet mengakibatkan hasil ringkasan yang kurang baik. Oleh karena itu dibutuhkan adanya eliminasi tweet yang tidak berkaitan dengan konteks sebelum dilakukan peringkasan. Kontribusi penelitian ini adalah adanya metode pemodelan topik sebagai bagian tahapan dalam serangkaian proses eliminasi data. Metode pemodelan topik sebagai salah satu teknik eliminasi data dapat digunakan dalam berbagai kasus namun pada penelitian ini difokuskan pada COVID-19. Tujuannya adalah untuk mempermudah masyarakat memperoleh informasi terkini secara ringkas. Tahapan yang dilakukan adalah pra-pemrosesan, eliminasi data menggunakan pemodelan topik dan peringkasan otomatis. Penelitian ini menggunakan kombinasi beberapa metode word embedding, pemodelan topik dan peringkasan otomatis sebagai pembanding. Ringkasan diuji menggunakan metode ROUGE dari setiap kombinasi untuk ditemukan kombinasi terbaik dari penelitian ini. Hasil pengujian menunjukkan kombinasi metode Word2Vec, LSI dan TextRank memiliki nilai ROUGE terbaik yaitu 0.67. Sedangkan kombinasi metode TFIDF, LDA dan Okapi BM25 memiliki nilai ROUGE terendah yaitu 0.35.

 

Abstract

Twitter accounts, such as Suara Surabaya, can help spread information about COVID-19 even though there are other topics such as accidents, traffic jams or other topics. Text summarization can be implemented in the case of reading Twitter data because of the large number of tweets available, making it easier to obtain the latest important information related to COVID-19. The number of discussion variations in the tweet text results in poor summary results. Therefore, it is necessary to eliminate tweets that are not related to the context before summarization is carried out. The contribution to this research is the topic modeling method as part of a series of data elimination processes. The topic modeling method as a data elimination technique can be used in various cases, but this research focuses on COVID-19. The aim is to make it easier for the public to obtain current information in a concise manner. The steps taken in this study were pre-processing, data elimination using topic modeling and automatic summarization. This study uses a combination of several word embedding methods, topic modeling and automatic summarization as a comparison. The summary is tested using the ROUGE method of each combination to find the best combination of this study. The test results show that the combination of Word2Vec, LSI and TextRank methods has the best ROUGE value, 0.67. While the combination of TFIDF, LDA and Okapi BM25 methods has the lowest ROUGE value, 0.35.


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Referensi


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DOI: http://dx.doi.org/10.25126/jtiik.0814324