Sentiment analysis: a comparison of deep learning neural network algorithm with SVM and naϊve Bayes for Indonesian text

Mariel, Wahyu Calvin Frans; Mariyah, Siti; Pramana, Setia

doi:10.1088/1742-6596/971/1/012049

Cited by 27 publications

(21 citation statements)

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“…SVM [ 37 ] and NB [ 38 ] are traditional machine learning methods that have been used in text classification tasks [ 13 , 14 , 15 ]. Some studies showed that the performance of SVM and NB is comparable to neural networks [ 20 , 21 ] while the opposite results were found in the other studies [ 39 , 40 ]. In this study, we used the term frequency-inverse document frequency method to vectorize the text data.…”

Section: Methodsmentioning

confidence: 96%

Applying Machine Learning to Identify Anti-Vaccination Tweets during the COVID-19 Pandemic

Huynh

et al. 2021

IJERPH

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Anti-vaccination attitudes have been an issue since the development of the first vaccines. The increasing use of social media as a source of health information may contribute to vaccine hesitancy due to anti-vaccination content widely available on social media, including Twitter. Being able to identify anti-vaccination tweets could provide useful information for formulating strategies to reduce anti-vaccination sentiments among different groups. This study aims to evaluate the performance of different natural language processing models to identify anti-vaccination tweets that were published during the COVID-19 pandemic. We compared the performance of the bidirectional encoder representations from transformers (BERT) and the bidirectional long short-term memory networks with pre-trained GLoVe embeddings (Bi-LSTM) with classic machine learning methods including support vector machine (SVM) and naïve Bayes (NB). The results show that performance on the test set of the BERT model was: accuracy = 91.6%, precision = 93.4%, recall = 97.6%, F1 score = 95.5%, and AUC = 84.7%. Bi-LSTM model performance showed: accuracy = 89.8%, precision = 44.0%, recall = 47.2%, F1 score = 45.5%, and AUC = 85.8%. SVM with linear kernel performed at: accuracy = 92.3%, Precision = 19.5%, Recall = 78.6%, F1 score = 31.2%, and AUC = 85.6%. Complement NB demonstrated: accuracy = 88.8%, precision = 23.0%, recall = 32.8%, F1 score = 27.1%, and AUC = 62.7%. In conclusion, the BERT models outperformed the Bi-LSTM, SVM, and NB models in this task. Moreover, the BERT model achieved excellent performance and can be used to identify anti-vaccination tweets in future studies.

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Section: Methodsmentioning

confidence: 96%

Applying Machine Learning to Identify Anti-Vaccination Tweets during the COVID-19 Pandemic

Huynh

et al. 2021

IJERPH

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“…Deep Neural Network adalah neural network yang tersusun dari layer yang jumlahnya lebih dari satu [11]. Penelitian yang menggunakan DNN dilakukan oleh [12] untuk menganalisa sentimen pada Twitter berbahasa Indonesia mengenai institusi pemerintahan dan tokoh pemerintahan. Penelitian tersebut membandingkan metode Deep Learning Neural Network dengan SVM dan Naïve Bayes.…”

Section: Pendahuluanunclassified

Analisis Sentimen Twitter untuk Menilai Opini Terhadap Perusahaan Publik Menggunakan Algoritma Deep Neural Network

Hidayat

Hardiansyah

Affandy

2021

TEKNOSI

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Dalam menaikkan kinerja serta mengevaluasi kualitas, perusahaan publik membutuhkan feedback dari masyarakat / konsumen yang bisa didapat melalui media sosial. Sebagai pengguna media sosial Twitter terbesar ketiga di dunia, tweet yang beredar di Indonesia memiliki potensi meningkatkan reputasi dan citra perusahaan. Dengan memanfaatkan algoritma Deep Neural Network (DNN), neural network yang tersusun dari layer yang jumlahnya lebih dari satu, didapati hasil analisa sentimen pada Twitter berbahasa Indonesia menjadi lebih baik dibanding dengan metode lainnya. Penelitian ini menganalisa sentimen melalui tweet dari masyarakat Indonesia terhadap sejumlah perusahaan publik dengan menggunakan DNN. Data Tweet sebanyak 5504 record didapat dengan melakukan crawling melalui Application Programming Interface (API) Twitter yang selanjutnya dilakukan preprocessing (cleansing, case folding, formalisasi, stemming, dan tokenisasi). Proses labeling dilakukan untuk 3902 record dengan memanfaatkan aplikasi Sentiment Strength Detection. Tahap pelatihan model dilakukan menggunakan algoritma DNN dengan variasi jumlah hidden layer, susunan node, dan nilai learning rate. Eksperimen dengan proporsi data training dan testing sebesar 90:10 memberikan hasil performa terbaik. Model tersusun dengan 3 hidden layer dengan susunan node tiap layer pada model tersebut yaitu 128, 256, 128 node dan menggunakan learning rate sebesar 0.005, model mampu menghasilkan nilai akurasi mencapai 88.72%.

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“…Building a text corpus has two main steps, namely collecting and preprocessing [14]. Text preprocessing is an early stage of semantic analysis (meaning accuracy) and syntactic analysis (arrangement accuracy) [15]. The steps in Indonesian text processing consist of; case folding, tokenizing, stopword removal, and stemming.…”

Section: Text Processingmentioning

confidence: 99%

Indonesian Words Error Detection System using Nazief Adriani Stemmer Algorithm

Yudhana¹,

Fadlil²,

Rosidin³

2019

IJACSA

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Stemming in each language has a different process and is determined according to the structure of the language. Stemming is mostly used as a complete step in the processing of words and phrases. There are many stemming algorithms available, and some used as a process for word processing. One function of stemming is to detect word errors in Indonesian. In this study, researchers created the Indonesian words error detection system using Nazief and Adriani algorithm. In the trials conducted, the system will accept text input obtained from the user. Then the system will preprocess the text. In this study, there are three stages of preprocessing, namely tokenization, case folding, and filtering. After the stages in preprocessing are finished, the system will call each word for the process of stemming. The results of the stemming will be compared with the base words available in the database. If it does not match, then the word is highlighted and is considered an error word. The first finding is the Nazief Adriani's algorithm can be able to detect words error until 100%. The second finding is the Nazief Adriani's algorithm also detect non-words error, the accuracy of detecting is 97.464%.

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Sentiment analysis: a comparison of deep learning neural network algorithm with SVM and naϊve Bayes for Indonesian text

Cited by 27 publications

References 0 publications

Applying Machine Learning to Identify Anti-Vaccination Tweets during the COVID-19 Pandemic

Applying Machine Learning to Identify Anti-Vaccination Tweets during the COVID-19 Pandemic

Analisis Sentimen Twitter untuk Menilai Opini Terhadap Perusahaan Publik Menggunakan Algoritma Deep Neural Network

Indonesian Words Error Detection System using Nazief Adriani Stemmer Algorithm

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