Crawling through web to extract the data from Social networking site - Twitter

Purohit, Narashima S.; Angadi, Akshata B.; Meghana, Bhat,; Gull, Karuna C.

doi:10.1109/parcomptech.2015.7084522

Cited by 6 publications

(2 citation statements)

References 4 publications

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“…In this section, we will discuss the results of the Twitter text mining trial for Topic classification on the online news web with a text mining approach using Latent Dirichlet Allocation (LDA). The trial was carried out with the aim of facilitating decision making in choosing topics to be made for news on the online news web [23].…”

Section: Resultsmentioning

confidence: 99%

Topic modeling using latent dirichlet allocation (LDA) on twitter data with Indonesia keyword

Negara,

Triadi

2022

Bull.Socinf.The.App

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Digital transformation causes an increase in the volume of information in the form of text such as news. On social media, a lot of news is uploaded in such a fast time and one of them is Twitter. Twitter is a social media service that has served many users, making it one of the social media that has very large data. From this very large data, it can be used as a news source for online news web. However, with the many topics extracted from Twitter data, the incoming data has a variety of topics which causes difficulties in identifying the topics from the data set taken and will require a lot of time if it has to be done manually by humans. Meanwhile, the data is potentially needed to provide information as quickly as possible. This study aims to classify topics on data taken from Twitter automatically so that it can make a classification on the news taken, can be more effective and efficient and does not take as much time as done manually by humans. The research was conducted using the Latent Dirichlet Allocation (LDA) method. News documents that will be classified are Indonesian news documents and will be classified into topics to be determined. The results of the research using topic modeling using the LDA method concluded that the number of topics formed from 9094 tweet data was 10 topics.

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Section: Resultsmentioning

confidence: 99%

Topic modeling using latent dirichlet allocation (LDA) on twitter data with Indonesia keyword

Negara,

Triadi

2022

Bull.Socinf.The.App

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“…Crawling [14] carried out tweet data collection with R Programming using R-Studio from Twitter. The data taken comprised only tweets in Indonesian, which consisted of 5,000 tweets containing the Jokowi keywords and 5000 tweets containing the Prabowo keywords, to give a total of 10,000 tweets.…”

Section: Tweet Data Collectionmentioning

confidence: 99%

The Implementation of the Machine Learning Algorithm for the Sentiment Analysis of Indonesia’s 2019 Presidential Election

Buntoro

Arifin

Syaifuddiin

et al. 2021

IIUMEJ

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In 2019, citizens of Indonesia participated in the democratic process of electing a new president, vice president, and various legislative candidates for the country. The 2019 Indonesian presidential election was very tense in terms of the candidates' campaigns in cyberspace, especially on social media sites such as Facebook, Twitter, Instagram, Google+, Tumblr, LinkedIn, etc. The Indonesian people used social media platforms to express their positive, neutral, and also negative opinions on the respective presidential candidates. The campaigning of respective social media users on their choice of candidates for regents, governors, and legislative positions up to presidential candidates was conducted via the Internet and online media. Therefore, the aim of this paper is to conduct sentiment analysis on the candidates in the 2019 Indonesia presidential election based on Twitter datasets. The study used datasets on the opinions expressed by the Indonesian people available on Twitter with the hashtags (#) containing "Jokowi and Prabowo." We conducted data pre-processing using a selection of comments, data cleansing, text parsing, sentence normalization and tokenization based on the given text in the Indonesian language, determination of class attributes, and, finally, we classified the Twitter posts with the hashtags (#) using Naïve Bayes Classifier (NBC) and a Support Vector Machine (SVM) to achieve an optimal and maximum optimization accuracy. The study provides benefits in terms of helping the community to research opinions on Twitter that contain positive, neutral, or negative sentiments. Sentiment Analysis on the candidates in the 2019 Indonesian presidential election on Twitter using non-conventional processes resulted in cost, time, and effort savings. This research proved that the combination of the SVM machine learning algorithm and alphabetic tokenization produced the highest accuracy value of 79.02%. While the lowest accuracy value in this study was obtained with a combination of the NBC machine learning algorithm and N-gram tokenization with an accuracy value of 44.94%. ABSTRAK: Pada tahun 2019 rakyat Indonesia telah terlibat dalam proses demokrasi memilih presiden baru, wakil presiden, dan berbagai calon legislatif negara. Pemilihan presiden Indonesia 2019 sangat tegang dalam kempen calon di ruang siber, terutama di laman media sosial seperti Facebook, Twitter, Instagram, Google+, Tumblr, LinkedIn, dll. Rakyat Indonesia menggunakan platfom media sosial bagi menyatakan pendapat positif, berkecuali, dan juga negatif terhadap calon presiden masing-masing. Kampen pencalonan menteri, gabenor, dan perundangan hingga pencalonan presiden dilakukan melalui media internet dan atas talian. Oleh itu, kajian ini dilakukan bagi menilai sentimen terhadap calon pemilihan presiden Indonesia 2019 berdasarkan kumpulan data Twitter. Kajian ini menggunakan kumpulan data yang diungkapkan oleh rakyat Indonesia yang terdapat di Twitter dengan hashtag (#) yang mengandungi "Jokowi dan Prabowo." Proses data dibuat menggunakan pilihan komentar, pembersihan data, penguraian teks, normalisasi kalimat, dan tokenisasi teks dalam bahasa Indonesia, penentuan atribut kelas, dan akhirnya, pengklasifikasian catatan Twitter dengan hashtag (#) menggunakan Klasifikasi Naïve Bayes (NBC) dan Mesin Vektor Sokongan (SVM) bagi mencapai ketepatan optimum dan maksimum. Kajian ini memberikan faedah dari segi membantu masyarakat meneliti pendapat di Twitter yang mengandungi sentimen positif, neutral, atau negatif. Analisis Sentimen terhadap calon dalam pemilihan presiden Indonesia 2019 di Twitter menggunakan proses bukan konvensional menghasilkan penjimatan kos, waktu, dan usaha. Penyelidikan ini membuktikan bahawa gabungan algoritma pembelajaran mesin SVM dan tokenisasi abjad menghasilkan nilai ketepatan tertinggi iaitu 79.02%. Manakala nilai ketepatan terendah dalam kajian ini diperoleh dengan kombinasi algoritma pembelajaran mesin NBC dan tokenisasi N-gram dengan nilai ketepatan 44.94%.

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