Analisis Sentimen Twitter Debat Calon Presiden Indonesia Menggunakan Metode  Fined-Grained Sentiment Analysis

Pratama, Septian; Andrean, Ricsa; Nugroho, Aryo

doi:10.31328/jointecs.v4i2.1004

Cited by 16 publications

(17 citation statements)

References 7 publications

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“…Popularitas teknik komputasi Sentiment Analysis juga pernah digunakan untuk memodelkan pendapat masyarakat berkaitan dengan debat calon presiden Indonesia tahun 2019 (Pratama, Andrean and Nugroho, 2019) Teknik sentiment analysis juga pernah dipergunakan untuk memodelkan kecenderungan pendapata masyarakat berkaitan dengan jasa transportasi online yaitu Gojek dan Grab (Pintoko and L., 2018). Dengan menggunakan hashtag #gojekindonesia dan #grab, data di-crawling mengguankan Twitter API.…”

Section: Penelitian Terkaitunclassified

Joint Distribution pada Weighted Majority Vote (WMV) untuk Peningkatan Kinerja Sentiment Analysis Tersupervisi pada Dataset Twitter

Rintyarna

2022

JTIIK

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Sentiment analysis adalah teknik komputasi text mining berbasis natural language processing (NLP) untuk mengekstraksi pendapat seseorang yang diungkapkan dalam platform online, termasuk dalam platform microblogging Twitter, salah satu platform microblogging yang paling popular digunakan di Indonesia. Ada dua pendekatan yang umum digunakan dalam teknik sentiment analysis yaitu pendekatan berbasis machine learning (ML) dan pendekatan berbasis sentiment lexicon (SL). Fokus penelitian ini adalah untuk pengembangan teknik sentiment analysis berbasis machine learning yang disebut juga teknik tersupervisi pada dataset Twitter. Sebagian besar sentiment analysis pada dataset Twitter berbahasa Indonesia mengandalkan single machine learning algorithm. Penelitian ini menggabungkan kinerja berbagai algoritma/experts seraya mengurangi tingkat kesalahan klasifikasi dengan meng-update bobot secara dinamis menggunakan weighted majority vote (WMV) berbasis joint distribution dari Bayesian Network. Pada tahap pertama, data di grabbing dari Twitter dengan 3 hashtag terkait Covid-19 sebagai data eksperimen. Selanjutnya kinerja weighted majority vote secara ekstensif dibandingkan dengan 4 metode baseline sebagai pembanding, yaitu: Naïve Bayes, Gaussian Naïve Bayes, Multinomial Naïve Bayes dan Majority Vote dari ketiga single classifier tersebut. Metrics kinerja yang digunakan adalah precision, recall, fmeasure, accuracy dan Mathews correlation coeficient (MCCC). Dalam eksperimen, terbukti bahwa WMV mampu meningkatkan kinerja sentiment analysis pada ketiga topik dataset dengan evaluator berbagai metrics kinerja sentiment analysis. AbstractSentiment analysis is a computational text mining technique based on natural language processing (NLP) to extract someone's opinion expressed in online platforms, including the Twitter microblogging platform, one of the most popular microblogging platforms used in Indonesia. There are two approaches that are commonly used in sentiment analysis techniques, namely the machine learning (ML) based approach and the sentiment lexicon (SL) based approach. The focus of this research is the development of machine learning-based sentiment analysis techniques which are also called supervised techniques on the Twitter dataset. Most of the sentiment analysis on the Indonesian language Twitter dataset relies on a single machine learning algorithm. This study combines the performance of various algorithms/experts while reducing the level of misclassification by updating the weights dynamically using a joint distribution-based weighted majority vote (WMV) from the Bayesian Network. In the first stage, data was grabbed from Twitter with 3 hashtags related to Covid-19 as experimental data. Furthermore, the performance of the weighted majority vote was extensively compared with 4 baseline methods for comparison, namely: Naïve Bayes, Gaussian Naïve Bayes, Multinomial Nave Bayes and Majority Vote from the three single classifiers. Performance metrics used are precision, recall, fmeasure, accuracy and Mathews correlation coeficient. In experiments, it is proven that WMV is able to improve sentiment analysis performance on the three dataset topics with various evaluators of sentiment analysis performance metrics.

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Section: Penelitian Terkaitunclassified

Joint Distribution pada Weighted Majority Vote (WMV) untuk Peningkatan Kinerja Sentiment Analysis Tersupervisi pada Dataset Twitter

Rintyarna

2022

JTIIK

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“…Analisis sentimen merupakan salah satu cara untuk mengukur akurasi dalam menentukan setimen. Ada banyak metode yang digunakan untuk mengukur akurasi pada sentimen analysis, diataranya Naïve Bayes Classifier [22], Support Vector Machine (SVM) [23], Decision Tree [24], Fined-Grained [25], K-nearest neighbors algorithm [26], dan lain sebagainya. Pada penelitian ini, membandingkan 2 metode yaitu Support Vector Machine (SVM) dan Naïve Bayes Classifier (NBC).…”

Section: A Pendahuluanunclassified

Analisis Pilkada Medan pada Sosial Media Menggunakan Analisis Sentimen dan Social Network Analyisis

Anam¹,

Firdaus

Fitri³

et al. 2022

ijcs

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The simultaneous regional head elections were over, but during the campaign until it was decided to become regional head there were many comments, both pro and contra. The city of Medan is one of the regions that will hold the 2020 ELECTION during the pandemic. The Medan City Election has decided that the pair Bobby Nasution and Aulia Rachman have won. This victory certainly gets a variety of comments on social media, especially Twitter. This study conducts sentiment analysis to see the sentiment that occurs, namely seeing negative, positive, or neutral comments. This sentiment analysis uses two methods to see the resulting accuracy, namely Support Vector Machine (SVM) and Naïve Bayes Classifier (NBC). This study also looks at the interactions that occur using Social Network Analysis (SNA). In addition to sentiment analysis and SNA, this study also looks at the existence of BOT accounts used in the #PilkadaMedan. The results obtained from the sentiment analysis show that NBC has the highest accuracy, which is 81, 72% with a data proportion of 90:10. Then on SNA, the @YanHarahap account got the highest nodes, namely 911 nodes. Then from 10326 tweets, 11% were suspected of being BOT by the DroneEmprit Academic system.

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“…Beberapa penelitian terdahulu juga melakukan analisis sentimen dengan mengulas hal-hal sebagai berikt : komparasi fitur seleksi [111]; kinerja KPU Pemilu [112]; kepuasan pengguna penyedia layanan komunikasi seluler [113]; kepuasan dalam proses pendidikan [114]; kehadiran Mass Rapid Transit (MRT) [115]; kebijakan pemindahan Ibukota Republik Indonesia [1], [116], [117]; kebijakan ganjil genap [118]- [120]; jasa ojek online [121], [122]; jasa ekspedisi barang [123]; impor beras [124]; hotel [125]; ujaran kebencian [6]; evaluasi kinerja dosen [126], [127]; Dewan Perwakilan Rakyat [128]; debat calon presiden [129]; debat calon gubernur [130]; dataset Indonesia [131]; cyberbullying [132]; calon gubernur [133]- [136]; berita kebakaran hutan [137]; asuransi [138]; acara televisi [139], [140]. Berdasarkan kuantitas riset, dapat diketahui bahwa ulasan sentimen analisis dengan topik kebijakan pemindahan ibukota Republik Indonesia, kebijakan ganjil genap, jasa ojek online, evaluasi kinerja dosen, calon gubernur dan acara televisi.…”

Section: Gambar 2 Hasil Klasifikasi Studi Kasus Kajian Analisis Sentimen Sumber : Hasil Penyandian Axial Menggunakan Nvivo 12 Plusunclassified

Pemilihan Metode dan Algoritma dalam Analisis Sentimen di Media Sosial : Sistematic Literature Review

Singgalen

2021

Journal-ISI

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This article use Systematic Literature Review (SLR) to classify sentiment analysis based on case studies, methods, social media, and platforms. The coding stage is divided into three stages, namely the open, selective and axial coding. The literature study on sentiment analysis is divided into two parts: identifying gaps based on case studies and data sources and identifying gaps based on the methods or algorithms used. The gap identification results based on case studies and data sources show that popular review topics are synonymous with entertainment, economic and political content. Therefore, the quantity of research with review topics related to the implementation of education, the dynamics of the bureaucracy, health facilities and services, and non-governmental organization’s activities need to be increased. Meanwhile, the most dominant platforms used as data sources are website and mobile-based applications. The results of the gap identification based on the method and algorithm show that the quantity of research with the Naive Bayes Classifier (NBC) and Support Vector Machine (SVM) method or algorithm is more dominant than the k-Nearest Neighbor (k-NN) and Lexicon-based. Thus, it is necessary to increase the number of other classification methods such as Particle Swarm Optimization, BM25, Decision Tree, K-Means, and Neural Networks.

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Analisis Sentimen Twitter Debat Calon Presiden Indonesia Menggunakan Metode Fined-Grained Sentiment Analysis

Cited by 16 publications

References 7 publications

Joint Distribution pada Weighted Majority Vote (WMV) untuk Peningkatan Kinerja Sentiment Analysis Tersupervisi pada Dataset Twitter

Joint Distribution pada Weighted Majority Vote (WMV) untuk Peningkatan Kinerja Sentiment Analysis Tersupervisi pada Dataset Twitter

Analisis Pilkada Medan pada Sosial Media Menggunakan Analisis Sentimen dan Social Network Analyisis

Pemilihan Metode dan Algoritma dalam Analisis Sentimen di Media Sosial : Sistematic Literature Review

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