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

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%.

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Sentiment Analysis on Government Performance in Tourism During The COVID-19 Pandemic Period With Lexicon Based

CAUCHY ◽

10.18860/ca.v7i1.12488 ◽

2021 ◽

Vol 7 (1) ◽

pp. 28-39

Author(s):

Adri Priadana ◽

Ahmad Ashril Rizal

Keyword(s):

Sentiment Analysis ◽

Naive Bayes ◽

Naïve Bayes ◽

Tourism Industry ◽

Naive Bayes Classifier ◽

Bayes Classifier ◽

Naïve Bayes Classifier ◽

SENTIMENT ANALYSIS OF TWITTER DATA ON REMOTE LEARNING USING NAÏVE BAYES ALGORITHM

Jurnal Riset Informatika ◽

10.34288/jri.v3i3.187 ◽

2021 ◽

Vol 3 (3) ◽

pp. 203-210

Author(s):

Putri Rana Khairina ◽

Desti Fitriati

Keyword(s):

Sentiment Analysis ◽

Naive Bayes ◽

Naïve Bayes ◽

Learning System ◽

Public Sentiment ◽

Global Pandemic ◽

Distance Learning System ◽

Twitter Users ◽

The Government ◽

Bayes Algorithm

Covid-19 is widespread, resulting in a global pandemic. Distance Learning System (DLS) is considered as a solution but, the reality of the implementation of DLS is not in accordance with the expectations of the community. Many Twitter users wrote their opinions on DLS. The tendency of public sentiment can be used as a way to improve the existing education system in Indonesia and can be an input for the government in improving the DLS method that is being implemented. Thus, this study produced a system that can analyze tweet sentiment towards DLS. The tweet was obtained using the Twitter API. The method used is Naïve Bayes for the process of classification of positive, negative, and neutral sentiments using 600 data. Then, data sharing is done 80% data training and 20% data testing that will be in the text preprocessing first. The accuracy of sentiment analysis of DLS using the Naïve Bayes method using 3-fold Cross-Validation produces an average of 93%.

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Klasifikasi Tahap Kematangan Pisang Ambon Berdasarkan Warna Menggunakan Naive Bayes

PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic ◽

10.33558/piksel.v5i2.268 ◽

2018 ◽

Vol 5 (2) ◽

pp. 60-67 ◽

Cited By ~ 1

Author(s):

Dwi Yulianto ◽

Retno Nugroho Whidhiasih ◽

Maimunah Maimunah

Keyword(s):

Naive Bayes ◽

Fruit Production ◽

Naïve Bayes ◽

Primary Data ◽

Banana Fruit ◽

Bayes Method ◽

Classification Image ◽

Average Accuracy ◽

The Government

ABSTRACT Banana fruit is a commodity that contributes a great value to both national and international fruit production achievement. The government through the National Standardization Agency establishes standards to maintain the quality of bananas. The purpose of this Project is to classify the stages of maturity of Ambon banana base on the color index using Naïve Bayes method in accordance with the regulations of SNI 7422:2009. Naive Bayes is used as a method in the classification process by comparing the probability values generated from the variable value of each model to determine the stage of Ambon banana maturity. The data used is the primary data image of 105 pieces of Ambon banana. By using 3 models which consists of different variables obtained the same greatest average accuracy by using the 2nd model which has 9 variable values (r, g, b, v, * a, * b, entropy, energy, and homogeneity) and the 3rd model has 7 variable values (r, g, b, v , * a, entropy and homogeneity) that is 90.48%. Keywords: banana maturity, classification, image processing ABSTRAK Buah pisang merupakan komoditas yang memberikan kontribusi besar terhadap angka produksi buah nasional maupun internasional. Pemerintah melalui Badan Standarisasi Nasional menetapkan standar untuk buah pisang, menjaga mutu buah pisang. Tujuan dari penelitian ini adalah klasifikasi tahapan kematangan dari buah pisang ambon berdasarkan indeks warna menggunakan metode Naïve Bayes sesuai dengan SNI 7422:2009. Naive bayes digunakan sebagai metode dalam proses pengklasifikasian dengan cara membandingkan nilai probabilitas yang dihasilkan dari nilai variabel penduga setiap model untuk menentukan tahap kematangan pisang ambon. Data yang digunakan adalah data primer citra pisang ambon sebanyak 105. Dengan menggunakan 3 buah model yang terdiri dari variabel penduga yang berbeda didapatkan akurasi rata-rata terbesar yang sama yaitu dengan menggunakan model ke-2 yang mempunyai 9 nilai variabel (r, g, b, v, *a, *b, entropi, energi, dan homogenitas) dan model ke-3 yang mempunyai 7 nilai variabel (r, g, b, v, *a, entropi dan homogenitas) yaitu sebesar 90.48%. Kata Kunci : kematangan pisang, klasifikasi, pengolahan citra

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Twitter Sentiment Analysis towards COVID-19 Vaccines in the Philippines Using Naïve Bayes

Information ◽

10.3390/info12050204 ◽

2021 ◽

Vol 12 (5) ◽

pp. 204

Author(s):

Charlyn Villavicencio ◽

Julio Jerison Macrohon ◽

X. Alphonse Inbaraj ◽

Jyh-Horng Jeng ◽

Jer-Guang Hsieh

Keyword(s):

Sentiment Analysis ◽

Language Processing ◽

Data Science ◽

Naive Bayes ◽

The Philippines ◽

Naïve Bayes ◽

Social Networking Site ◽

Bayes Model ◽

The Government ◽

Processing Techniques

A year into the COVID-19 pandemic and one of the longest recorded lockdowns in the world, the Philippines received its first delivery of COVID-19 vaccines on 1 March 2021 through WHO’s COVAX initiative. A month into inoculation of all frontline health professionals and other priority groups, the authors of this study gathered data on the sentiment of Filipinos regarding the Philippine government’s efforts using the social networking site Twitter. Natural language processing techniques were applied to understand the general sentiment, which can help the government in analyzing their response. The sentiments were annotated and trained using the Naïve Bayes model to classify English and Filipino language tweets into positive, neutral, and negative polarities through the RapidMiner data science software. The results yielded an 81.77% accuracy, which outweighs the accuracy of recent sentiment analysis studies using Twitter data from the Philippines.

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Aspect Based Sentiment Analysis of Nepali Text Using Support Vector Machine and Naive Bayes

Technical Journal ◽

10.3126/tj.v2i1.32824 ◽

2020 ◽

Vol 2 (1) ◽

pp. 22-29

Author(s):

Sujan Tamrakar ◽

Bal Krishna Bal ◽

Rajendra Bahadur Thapa

Keyword(s):

Support Vector Machine ◽

Sentiment Analysis ◽

Naive Bayes ◽

Naïve Bayes ◽

Support Vector ◽

Svm Classifier ◽

Pos Tagging ◽

Part Of Speech ◽

Document Frequency ◽

Classifier Algorithms

Aspect-based Sentiment Analysis assists in understanding the opinion of the associated entities helping for a better quality of a service or a product. A model is developed to detect the aspect-based sentiment in Nepali text using Machine Learning (ML) classifier algorithms namely Support Vector Machine (SVM) and Naïve Bayes (NB). The system collects Nepali text data from various websites and Part of Speech (POS) tagging is applied to extract the desired features of aspect and sentiment. Manual labeling is done for each sentence to identify the sentiment of the sentence. Term Frequency – Inverse Document Frequency (TF-IDF) is applied to compute the importance of the words. The feature vectors thus produced are then applied to the Classifier algorithms to predict and classify the sentence. The accuracy obtained by the SVM classifier is 76.8% whereas Bernoulli NB is 77.5%.

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Analisis Perbandingan Algoritma Decision Tree, kNN, dan Naive Bayes untuk Prediksi Kesuksesan Start-up

JISKA (Jurnal Informatika Sunan Kalijaga) ◽

10.14421/jiska.2021.6.3.178-188 ◽

2021 ◽

Vol 6 (3) ◽

pp. 178-188

Author(s):

Adhitya Prayoga Permana ◽

Kurniyatul Ainiyah ◽

Khadijah Fahmi Hayati Holle

Keyword(s):

Decision Tree ◽

Naive Bayes ◽

Naïve Bayes ◽

Test Results ◽

Decision Tree Algorithm ◽

Tree Algorithm ◽

Start Up ◽

The Many ◽

Start Ups

Start-ups have a very important role in economic growth, the existence of a start-up can open up many new jobs. However, not all start-ups that are developing can become successful start-ups. This is because start-ups have a high failure rate, data shows that 75% of start-ups fail in their development. Therefore, it is important to classify the successful and failed start-ups, so that later it can be used to see the factors that most influence start-up success, and can also predict the success of a start-up. Among the many classifications in data mining, the Decision Tree, kNN, and Naïve Bayes algorithms are the algorithms that the authors chose to classify the 923 start-up data records that were previously obtained. The test results using cross-validation and T-test show that the Decision Tree Algorithm is the most appropriate algorithm for classifying in this case study. This is evidenced by the accuracy value obtained from the Decision Tree algorithm, which is greater than other algorithms, which is 79.29%, while the kNN algorithm has an accuracy value of 66.69%, and Naive Bayes is 64.21%.

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Klasifikasi sinopsis novel menggunakan metode naïve bayes classifier

Repositor ◽

10.22219/repositor.v1i2.799 ◽

2019 ◽

Vol 1 (2) ◽

pp. 125

Author(s):

Vinna Rahmayanti ◽

Setio Basuki ◽

Hilman Hilman

Keyword(s):

Naive Bayes ◽

Confusion Matrix ◽

Naïve Bayes ◽

Training Data ◽

The Novel ◽

Naive Bayes Classifier ◽

Bayes Classifier ◽

Naïve Bayes Classifier ◽

Romantic Comedy ◽

Document Frequency

It is undeniable that technological progress is developing very quickly in the field of computers, now with computers the work that was originally done by humans can be taken over by computers to help human work itself, like case studi of this research is a system that can classification the text like synopsis into genre group. Genre is the style of story in a novel, there are many genres in the novel that are expected to be romantic, comedy, mystery, horror and others, by knowing the genre of the novel the reader will be able to know the story style of the novel. The method used in this research is TF-IDF (Term Frequency Inverse Document Frequency) and Naïve Bayes Classifier. The TF-IDF method is used to get the weight of each word contained in the resulting document is used in the Naïve Bayes Classifier method to get the synopsis classification results into genre. Based on the evaluation using a confusion matrix using 600 training data and 200 test data obtained an accuracy of 80.5%.AbstractIt is undeniable that technological progress is developing very quickly in the field of computers, now with computers the work that was originally done by humans can be taken over by computers to help human work itself, like case studi of this research is a system that can classification the text like synopsis into genre group. Genre is the style of story in a novel, there are many genres in the novel that are expected to be romantic, comedy, mystery, horror and others, by knowing the genre of the novel the reader will be able to know the story style of the novel. The method used in this research is TF-IDF (Term Frequency Inverse Document Frequency) and Naïve Bayes Classifier. The TF-IDF method is used to get the weight of each word contained in the resulting document is used in the Naïve Bayes Classifier method to get the synopsis classification results into genre. Based on the evaluation using a confusion matrix using 600 training data and 200 test data obtained an accuracy of 80.5%.

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PREDIKSI PEMILIHAN JURUSAN DIPERGURUAN TINGGI

AL-JAZARI JURNAL ILMIAH TEKNIK MESIN ◽

10.31602/al-jazari.v3i2.1617 ◽

2018 ◽

Vol 3 (2) ◽

Author(s):

Dian Agustini ◽

Muthia Farida ◽

Auliya Rahman

Keyword(s):

High School Students ◽

Naive Bayes ◽

Naïve Bayes ◽

User Preferences ◽

Limited Information ◽

Prospective Students ◽

School Students ◽

The Government ◽

Bayes Algorithm ◽

Selection Of

The education sector is one of the fields that gets the most attention from the government, especially when graduating from high school students. It is expected that these graduates will continue to pursue higher education. Various information about majors in universities have been widely available but have not been able to meet the needs of prospective students. There are three main problems experienced by prospective students, namely limited knowledge of the majors to be followed, limited information available, and limited quantitative recommendations that can be used by prospective students.This study tries to overcome these problems by producing predictions of departmental recommendations using the Naive Bayes algorithm and incorporating criteria that influence the selection of majors in the form of abilities, interests, and also preferences for certain fields. An approach to user preferences is used so that the recommendations approach the desired results. This is done by giving the criteria weighting to the user. Keywords: Data Mining, Predictions, Universities, Naive Bayes

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Twitter Sentiment Analysis using Machine Learning Techniques

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.c6281.029320 ◽

2020 ◽

Vol 9 (3) ◽

pp. 4205-4209

Keyword(s):

Logistic Regression ◽

Sentiment Analysis ◽

Naive Bayes ◽

Naïve Bayes ◽

Machine Learning Techniques ◽

Naive Bayes Classifier ◽

Bayes Classifier ◽

Naïve Bayes Classifier ◽

Learning Techniques ◽

Social Media Platforms

Nowadays people share their views and opinions in twitter and other social media platforms, the way of recognizing sentiments and speculation in tweets is Twitter Sentiment Analysis. Determining the contradiction or sentiment of the tweets and then listing them into positive, negative and neutral tweets is the main classifying step in this process. The issue related to sentiment analysis is the naming of the correct congruous sentiment classifier algorithm to list the tweets. The foundation classifier techniques like Logistic regression, Naive Bayes classifier, Random Forest and SVMs are normally used. In this paper, the Naïve Bayes classifier and Logistic Regression has been used to perform sentiment analysis and classify based on the better accuracy of catagorizing Technique. The outcome shows that Naive Bayes classifier works better for this approach. Data pre-processing and feature extraction is realized as a portion of task.

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Analisis Sentimen Multi-Aspek Berbasis Konversi Ikon Emosi dengan Algoritme Naïve Bayes untuk Ulasan Wisata Kuliner Pada Web Tripadvisor

Jurnal Teknologi Informasi dan Ilmu Komputer ◽

10.25126/jtiik.2020731907 ◽

2020 ◽

Vol 7 (4) ◽

pp. 737

Author(s):

Sitti Aliyah Azzahra ◽

Arief Wibowo

Keyword(s):

Data Mining ◽

Naive Bayes ◽

Naïve Bayes ◽

Emotional Expressions ◽

Tourist Attraction ◽

Test Results ◽

Tourist Attractions ◽

Bayes Algorithm ◽

Labeling Method ◽

The City

Wisatawan seringkali mencari informasi tentang obyek wisata pada situs web seperti TripAdvisor. Situs web TripAdvisor memiliki fitur bagi penguna terdaftar untuk memberi ulasan tentang objek wisata dalam kategori kuliner dari berbagai negara. Ulasan tersebut bisa digunakan wisatawan sebagai pertimbangan sebelum mendatangi objek wisata kuliner yang ingin dituju. Komentar atau ulasan yang ada di situs TripAdvisor dapat dianalisis untuk mengetahui nilai sentimen dari suatu obyek wisata yang diulas. Hasil analisis itu dapat bermanfaat bagi pengelola tempat wisata, pengusaha kuliner maupun bagi wisatawan lain. Ada tantangan yang ditemukan saat analisis sentimen dilakukan pada kalimat ulasan yang mengandung ikon emosi atau emoticon, karena ulasan dapat mengandung arti sentimen yang berbeda antara kalimat dengan ekspresi emosi yang ada. Penelitian ini berisi analisis ulasan tentang kuliner kota Bandung pada situs TripAdvisor yang mengklasifikasi sentimen menjadi tiga kelas. Penelitian ini menggunakan teknik klasifikasi data mining dengan algoritme Naïve Bayes dikombinasi dengan metode pelabelan multi aspek yang disertai konversi ikon emosi pada teks ulasan. Selain itu, analisis dilakukan pada bobot ulasan berdasarkan jumlah kontribusi pemberi ulasan di web TripAdvisor. Hasil pengujian menunjukkan bahwa penggunaan seluruh kombinasi metode tersebut dalam proses klasifikasi sentimen mampu menghasilkan nilai akurasi sebesar 98,67%. AbstractTourists often look for information about attractions on websites such as TripAdvisor. The TripAdvisor website has a feature for registered users to provide reviews about attractions in the culinary category from various countries. These reviews can be used by tourists as a consideration before visiting culinary attractions to be addressed. Comments or reviews on the TripAdvisor site can be analyzed to determine the sentiment value of a tourist attraction being reviewed. The results of the analysis can be useful for managers of tourist attractions, culinary entrepreneurs and for other tourists. There are challenges that are found when sentiment analysis is carried out on review sentences that contain emotion icons or emoticons, because reviews may contain different sentiment meanings between sentences and existing emotional expressions. This study contains a review of the culinary analysis of the city of Bandung on the TripAdvisor site which classifies sentiments into three classes. This study uses data mining classification techniques with the Naïve Bayes algorithm combined with a multi-aspect labeling method accompanied by the conversion of emotional icons in the review text. In addition, the analysis is carried out on the weight of the review based on the number of contributing reviewers on the TripAdvisor web. The test results show that the use of all combinations of these methods in the sentiment classification process is able to produce an accuracy value of 98.67%.

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