Analisis Sentimen Tweet Vaksin COVID-19 Menggunakan Recurrent Neural Network dan Naïve Bayes

Abstract

COVID-19 has become a global pandemic including Indonesia, so the government is taking vaccinations as a preventive measure. The public's response to this continues to appear on social media platforms, one of which is Twitter. Tweets about the COVID-19 vaccine have generated various kinds of positive and negative opinions in the community. Therefore, it is very important to detect and filter it to prevent the spread of incorrect information. Sentiment analysis is a method used to determine the content of a dataset in the form of negative, positive or neutral text. The dataset in this study was obtained from 5000 COVID-19 vaccine tweets with the distribution of 3800 positive sentiment tweets, 800 negative sentiment tweets and 400 neutral sentiment tweets. The dataset obtained is then pre-processed data to optimize data processing. There are 4 stages of pre-processing, including remove punctuation, case folding, stemming and tokenizing. This study examines the performance of RNN and Naïve Bayes by adding the TF-IDF (Term Frequency-Inverse Document Frequency) technique which aims to give weight to the word relationship (term) of a document. The test results show that RNN (TF-IDF) has a greater accuracy of 97.77% compared to Naïve Bayes (TF-IDF) of 80%.

Analisis Sentimen Terhadap Pangan Nasional Pada Media Sosial Twitter Menggunakan Algoritma Naïve Bayes

“…Tf-idf adalah salah satu cara untuk mengatasi keterbatasan ini adalah dengan menekankan hubungan kata (term) dalam dokumen. [14]. Frekuensi kata yang muncul dalam dokumen menunjukkan pentingnya kata tersebut.…”

“…Naive Bayes merupakan algoritma klasifikasi yang didasarkan pada teorema Bayes dan adjective naive didasarkan pada asumsi bahwa karakteristik data yang tidak bergantung satu sama lain [20]. Teknik klasifikasi Naïve Bayes merupakan metode lama yang masih sangat populer dalam proses analisis sentimen [14]. Ada banyak penelitian yang menggunakan algoritma naive bayes untuk kalsifikasi sentimen.…”

“…Preprocessing Pre-processing merupakan langkah-langkah dalam penelitian yang memperbaiki data sehingga lebih terstruktur untuk diproses di setiap dokumen yang digunakan [18] Naive Bayes merupakan algoritma klasifikasi yang didasarkan pada teorema Bayes dan adjective naive didasarkan pada asumsi bahwa karakteristik data yang tidak bergantung satu sama lain [20]. Teknik klasifikasi Naïve Bayes merupakan metode lama yang masih sangat populer dalam proses analisis sentimen [14] ada banyak penelitian yang menggunakan algoritma naive bayes untuk kalsifikasi sentimen. Keunggulan naive bayes adalah tidak membutuhkan data pelatihan yang besar untuk menentukan estimasi parameter yang dibutuhkan dalam proses klasifikasi.…”

Emotional Text Classification Using TF-IDF (Term Frequency-Inverse Document Frequency) And LSTM (Long Short-Term Memory)

“…Several prior studies on NLP have yielded positive results and responses in its application. There have been several earlier NLP studies focusing on sentiment analysis utilizing Twitter data [3], scientific sentence classification [2], [4][5][6][7], and NLP for opinion mining [8].…”

“…However, vectorizer standards such as Count vectorizer do not take into account label information when creating text vectors. The TF-IDF (Term Frequency-Inverse Document Frequency) technique aims to overcome this limitation by giving weight to the relationship of a word (term) in a document [7], [12].…”