Analisis Sentimen Berbasis Emoticon pada Komentar Instagram Bahasa Indonesia Menggunakan Naïve Bayes

The usage of social media growing rapidly, especially after the smartphone was invented. Because the number of social media users was quite a lot, companies prefer to promote their products through social media like Instagram. But, unlike TV or radio, social media is a two-way communication media, that makes users can respond directly to the content created by the company. Comments given by users have various types of sentiment, like positive or negative comments. In addition to using text, comments also often contain emoticons to support the message. This study tries to analyzing sentiment based on the usage of emoticons inside them using the Naïve Bayes algorithm. Based on the test results, the accuracy result is quite good, it is about 96.3% correct in sentiment classification.

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Tweet Netizen Prediction Using Random Forest, Decision Tree, Naïve Bayes, And Ensemble Algorithm (Case Study The Governor Of DKI Jakarta)

SinkrOn ◽

10.33395/sinkron.v5i1.10565 ◽

2020 ◽

Vol 5 (1) ◽

pp. 9-20

Author(s):

Antonius Yadi Kuntoro

Keyword(s):

Social Media ◽

Random Forest ◽

Decision Tree ◽

Naive Bayes ◽

Naïve Bayes ◽

Training Data ◽

Testing Data ◽

Negative Comments ◽

Ensemble Algorithm ◽

Bayes Algorithm

Abstract — The current Governor of DKI Jakarta, even though he has been elected since 2017 is always interesting to talk about or even comment on. Comments that appear come from the media directly or through social media. Twitter has become one of the social media that is often used as a media to comment on elected governors and can even become a trending topic on Twitter social media. Netizens who comment are also varied, some are always Tweeting criticism, some are commenting Positively, and some are only re-Tweeting. In this research, a prediction of whether active Netizens will tend to always lead to Positive or Negative comments will be carried out in this study. Model algorithms used are Decision Tree, Naïve Bayes, Random Forest, and also Ensemble. Twitter data that is processed must go through preprocessing first before proceeding using Rapidminer. In trials using Rapidminer conducted in four trials by dividing into two parts, namely testing data and training data. Comparisons made are 10% testing data: 90% Training data, then 20% testing data: 80% training data, then 30% testing data: 70% training data, and the last is 35% testing data: 65% training data. The average Accuracy for the Decision Tree algorithm is 93.15%, while for the Naïve Bayes algorithm the Accuracy is 91.55%, then for the Random Forest algorithm is 93.41, and the last is the Ensemble algorithm with an Accuracy of 93, 42%. here. Keywords — Decision Tree, Naïve Bayes, Random Forest, Set, Twitter.

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Sentiment Analysis Pemutusan Hubungan Kerja Akibat Pandemi Covid-19 Menggunakan Algoritma NaïveBayes Dan PSO

Jurnal Sisfokom (Sistem Informasi dan Komputer) ◽

10.32736/sisfokom.v10i3.1299 ◽

2021 ◽

Vol 10 (3) ◽

pp. 426-431

Author(s):

Wiyanto Wiyanto ◽

Zulita Setyaningsih

Keyword(s):

Social Media ◽

Feature Selection ◽

Data Collection ◽

Sentiment Analysis ◽

Naive Bayes ◽

Poverty Rate ◽

Naïve Bayes ◽

Test Results ◽

Text Preprocessing ◽

Bayes Algorithm

The Pandemic Covid-19 in Indonesia in 2020 had an impact on Termination of Employment (PHK), this has received various public opinions on social media. At a time when the poverty rate is high and unemployment increases every year, it becomes a factor of public disapproval of Termination of Employment (PHK). It is necessary to classify public opinion into a negative opinion or a positive opinion on this issue. The purpose of this study is to analyze the sentiment towards layoffs to determine negative or positive opinions using the Naïve Bayes algorithm by adding feature selection. The research stages consist of data collection, text preprocessing, feature selection, and application of algorithms. The testing process in this study uses the Rapid Miner application. The test results in this study using the Naive Bayes Algorithm, the accuracy value is 93.57% and for addition to the Naïve Bayes + PSO feature selection, the accuracy value is 93.71%. The best accuracy value in sentiment analysis of layoffs in the covid-19 pandemic is the addition of the PSO feature selection in the Naïve Bayes Algorithm, which is 0.14% better.

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Analysis of Sentiment of Moving a National Capital with Feature Selection Naive Bayes Algorithm and Support Vector Machine

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) ◽

10.29207/resti.v4i3.1942 ◽

2020 ◽

Vol 4 (3) ◽

pp. 504-512

Author(s):

Faried Zamachsari ◽

Gabriel Vangeran Saragih ◽

Susafa'ati ◽

Windu Gata

Keyword(s):

Social Media ◽

Support Vector Machine ◽

Feature Selection ◽

Public Opinion ◽

Naive Bayes ◽

Naïve Bayes ◽

Capital City ◽

Support Vector ◽

National Capital ◽

Bayes Algorithm

The decision to move Indonesia's capital city to East Kalimantan received mixed responses on social media. When the poverty rate is still high and the country's finances are difficult to be a factor in disapproval of the relocation of the national capital. Twitter as one of the popular social media, is used by the public to express these opinions. How is the tendency of community responses related to the move of the National Capital and how to do public opinion sentiment analysis related to the move of the National Capital with Feature Selection Naive Bayes Algorithm and Support Vector Machine to get the highest accuracy value is the goal in this study. Sentiment analysis data will take from public opinion using Indonesian from Twitter social media tweets in a crawling manner. Search words used are #IbuKotaBaru and #PindahIbuKota. The stages of the research consisted of collecting data through social media Twitter, polarity, preprocessing consisting of the process of transform case, cleansing, tokenizing, filtering and stemming. The use of feature selection to increase the accuracy value will then enter the ratio that has been determined to be used by data testing and training. The next step is the comparison between the Support Vector Machine and Naive Bayes methods to determine which method is more accurate. In the data period above it was found 24.26% positive sentiment 75.74% negative sentiment related to the move of a new capital city. Accuracy results using Rapid Miner software, the best accuracy value of Naive Bayes with Feature Selection is at a ratio of 9:1 with an accuracy of 88.24% while the best accuracy results Support Vector Machine with Feature Selection is at a ratio of 5:5 with an accuracy of 78.77%.

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The Sentiment Analysis Reviewing Indosat Services from Twitter Using the Naive Bayes Classifier

Journal of Applied Computer Science and Technology ◽

10.52158/jacost.v1i2.79 ◽

2020 ◽

Vol 1 (2) ◽

pp. 61-66

Author(s):

Febri Astiko ◽

Achmad Khodar

Keyword(s):

Machine Learning ◽

Social Media ◽

Sentiment Analysis ◽

Naive Bayes ◽

Learning Model ◽

Naïve Bayes ◽

Bayes Classifier ◽

Naïve Bayes Classifier ◽

Machine Learning Model ◽

Bayes Algorithm

This study aims to design a machine learning model of sentiment analysis on Indosat Ooredoo service reviews on social media twitter using the Naive Bayes algorithm as a classifier of positive and negative labels. This sentiment analysis uses machine learning to get patterns an model that can be used again to predict new data.

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SENTIMEN ANALISIS KEBIJAKAN GANJIL GENAP DI TOL BEKASI MENGGUNAKAN ALGORITMA NAIVE BAYES DENGAN OPTIMALISASI INFORMATION GAIN

Jurnal Pilar Nusa Mandiri ◽

10.33480/pilar.v15i2.705 ◽

2019 ◽

Vol 15 (2) ◽

pp. 247-254

Author(s):

Heru Sukma Utama ◽

Didi Rosiyadi ◽

Dedi Aridarma ◽

Bobby Suryo Prakoso

Keyword(s):

Social Media ◽

Opinion Mining ◽

Naive Bayes ◽

Information Gain ◽

Confusion Matrix ◽

Naïve Bayes ◽

Support Vector ◽

Toll Road ◽

Textual Data ◽

Bayes Algorithm

Analysis of the odd even-numbered sentiment systems in Bekasi toll using the Naïve Bayes Algorithm, is a process of understanding, extracting, and processing textual data automatically from social media. The purpose of this study was to determine the level of accuracy, recall and precision of opinion mining generated using the Naïve Bayes algorithm to provide information community sentiment towards the effectiveness of the odd system of Bekasi tiolls on social media. The research method used in this study was to do text mining in comments-comments regarding posts regarding even odd oddities on Bekasi toll on Twitter, Instagram, Youtube and Facebook. The steps taken are starting from preprocessing, transformation, datamining and evaluation, followed by information gaon feature selection, select by weight and applying NB Algorithm model. The results obtained from the study using the NB model are obtained Confusion Matrix result, namely accuracy of 79,55%, Precision of 80,51%, and Sensitivity or Recall of 80,91%. Thus this study concludes that the use of Support Vector Machine Algorithms can analyze even odd sentiments on the Bekasi toll road.

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Application of Naïve Bayes Algorithm in Sentiment Analysis of Filipino, English and Taglish Facebook Comments

Regular issue - International Journal of Management and Humanities ◽

10.35940/ijmh.e0524.014520 ◽

2020 ◽

Vol 4 (5) ◽

pp. 73-77

Keyword(s):

Social Media ◽

Sentiment Analysis ◽

Language Processing ◽

Opinion Mining ◽

Naive Bayes ◽

Naïve Bayes ◽

Product Reviews ◽

Documentary Data ◽

The Social ◽

Bayes Algorithm

The World Wide Web has boosted its content for the past years, it has a vast amount of multimedia resources that continuously grow specifically in documentary data. One of the major contributors of documentary contents can be evidently found on the social media called Facebook. People or netizens on Facebook are actively sharing their opinion about a certain topic or posts that can be related to them or not. With the huge amount of accessible documentary data that are seen on the so-called social media, there are research trends that can be made by the researchers in the field of opinion mining. A netizen’s comment on a particular post can either be a negative or a positive one. This study will discuss the opinion or comment of a netizen whether it is positive or negative or how she/he feels about a specific topic posted on Facebook; this is can be measured by the use of Sentiment Analysis. The combination of the Natural Language Processing and the analytics in textual form is also known as Sentiment Analysis that is use to the extraction of data in a useful manner. This study will be based on the product reviews of Filipinos in Filipino, English and Taglish (mixed Filipino and English) languages. To categorize a comment effectively, the Naïve Bayes Algorithm was implemented to the developed web system.

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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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Pengaruh N-Gram terhadap Klasifikasi Buku menggunakan Ekstraksi dan Seleksi Fitur pada Multinomial Naïve Bayes

JURNAL MEDIA INFORMATIKA BUDIDARMA ◽

10.30865/mib.v5i1.2672 ◽

2021 ◽

Vol 5 (1) ◽

pp. 264

Author(s):

Esti Mulyani ◽

Fachrul Pralienka Bani Muhamad ◽

Kurnia Adi Cahyanto

Keyword(s):

Naive Bayes ◽

Automatic Classification ◽

Naïve Bayes ◽

Main Task ◽

Test Results ◽

Book Title ◽

Feature Extraction And Selection ◽

N Gram ◽

Bayes Algorithm

Libraries have the main task in the processing of library materials by classifying books according to certain ways. Dewey Decimal Classification (DDC) is the method most commonly used in the world to determine book classification (labeling) in libraries. The advantages of this DDC method are universal and more systematic. However, this method is less efficient considering the large number of books that must be classified in a library, as well as labeling that must follow label updates on the DDC. An automatic classification system will be the perfect solution to this problem. Automatic classification can be done by applying the text mining method. In this study, searching for words in the book title was carried out with N-Gram (Unigram, Bigram, Trigram) as a feature generation. The features that have been raised are then selected for features. The process of book title classification is carried out using the Naïve Bayes Multinomial algorithm. This study examines the effect of Unigram, Bigram, Trigram on the classification of book titles using the feature extraction and selection feature on Multinomial Naïve Bayes algorithm. The test results show Unigram has the highest accuracy value of 74.4%.

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