Text Mining and Support Vector Machine for Sentiment Analysis of Tourist Reviews in Bangkalan Regency

Imamah, Imamah; Husni,; Rachman, Eka Malasari; Suzanti, Ika Oktavia; Mufarroha, Fifin Ayu

doi:10.1088/1742-6596/1477/2/022023

Cited by 9 publications

(4 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Machine learning with the support vector machine method (Imamah, Husni, Rachman, Suzanti, & Mufarroha, 2020 (2) The second approach was lexicon-based (Jurek, Mulvenna, & Bi, 2015), which used various words that have been valued with the polarity score to determine the response of the users or the public. The lexicon sentiment calculated the sentiment from the semantic orientations of the words and phrases that came up in the text (Taboada, Brooke, Tofiloski, Voll, & Stede, 2011).…”

Section: Methodsmentioning

confidence: 99%

Applying bag of words approach to determine remote sensing technology acceptance among smallholder plantations

Diana

Farida

2023

AGJSR

View full text Add to dashboard Cite

PurposeTechnology acceptance is a measure of that technology’s usefulness. Oil palm is one of the biggest contributors to Indonesia’s revenues, thus fueling its economy. Using remote sensing would allow a plantation to monitor and forecast its production and the amount of fertilizer used. This review aims to provide a policy recommendation in the form of a strategy to improve the added value of Indonesia’s oil palm and support the government in increasing oil palm production. This recommendation needs to be formulated by determining the users’ acceptance of remote sensing technology (state-owned plantations, private plantation companies and smallholder plantations).Design/methodology/approachThis review’s methodology used sentiment analysis through text mining (bag of words model). The study’s primary data were from focus group discussions (FGDs), questionnaires, observations on participants, audio-visual documentation and focused discussions based on group category. The results of interviews and FGDs were transcribed into text and analyzed to 1) find words that can represent the content of the document; 2) classify and determine the frequency (word cloud); and finally 3) analyze the sentiment.FindingsThe result showed that private plantation companies and state-owned plantations had extremely high positive sentiments toward using remote sensing in their oil palm plantations, whereas smallholders had a 60% resistance. However, there is still a possibility for this technology’s adoption by smallholders, provided it is free and easily applied.Research limitations/implicationsBasically, technology is applied to make work easier. However, not everyone is tech-savvy, especially the older generations. One dimension of technology acceptance is user/customer retention. New technology would not be immediately accepted, but there would be user perceptions about its uses and ease. At first, people might be reluctant to accept a new technology due to the perception that it is useless and difficult. Technology acceptance is the gauge of how useful technology is in making work easier compared to conventional ways.Practical implicationsTherefore, technology acceptance needs to be improved among smallholders by intensively socializing the policies, and through dissemination and dedication by academics and the government.Social implicationsThe social implications of using technology are reducing the workforce, but the company will be more profitable and efficient.Originality/valueRemote sensing is one of the topics that people have not taken up in a large way, especially sentiment analysis. Acceptance of technology that utilizes remote sensing for plantations is very useful and efficient. In the end, company profits can be allocated more toward empowering the community and the environment.

show abstract

Section: Methodsmentioning

confidence: 99%

Applying bag of words approach to determine remote sensing technology acceptance among smallholder plantations

Diana

Farida

2023

AGJSR

View full text Add to dashboard Cite

show abstract

“…Media sosial menjadi salah satu sumber data dari penelitian yang berkaitan dengan data science. Penelitian sebelumnya telah menggunakan media sosial Twitter untuk mengetahui opini masyarakat tentang tempat wisata di kabupaten Bangkalan [1]. Hasil dari penelitian ini dapat memetakan sentimen positif dan negatif dari wisatawan, sehingga dapat dijadikan rujukan untuk pengembangan tempat wisata di masa depan.…”

Section: Pendahuluanunclassified

“…SVM adalah teknik klasifikasi yang digunakan untuk menganalisa data dan memprediksi kelas berdasarkan pola [1]. Teknik klasifikasi pada SVM dilakukan dengan membentuk hyperplane atau garis pembatas (decision boundary).…”

Section: E Support Vector Machine (Svm)unclassified

Pendekatan Data Science untuk Mengukur Empati Masyarakat terhadap Pandemi Menggunakan Analisis Sentimen dan Seleksi Fitur

Rachman

Imamah

2022

JEPIN

View full text Add to dashboard Cite

Empati merupakan kemampuan seseorang untuk turut merasakan penderitaan orang lain. Pandemi covid yang melanda dunia, telah menyisakan banyak kehilangan dan keterpurukan. Penelitian ini bertujuan untuk mengetahui emosi masyarakat terhadap penderitaan sesama menggunakan pendekatan sentimen analisis. Dataset yang digunakan adalah komentar masyarakat di Twitter tentang pandemi Covid dalam rentang waktu November-Desember 2020. Data diambil dengan teknik crawling menggunakan library twint, didapatkan data sebanyak 2386 komentar, namun komentar yang mengandung empati hanya sebanyak 984 data. Dataset empati kemudian dilabeli oleh tiga orang menggunakan teknik majority voting. Hasil pengukuran dataset empati menunjukkan 55,7% komentar masyarakat indonesia mengandung empati positif (berempati), 37,4% empati negatif (tidak berempati), dan 6,9% netral. Untuk membentuk model yang dapat mendeteksi empati secara otomatis, maka digunakan dataset empati sebanyak 400, dengan 200 kelas positif dan 200 kelas negatif, kelas netral tidak digunakan pada penelitian ini karena jumlah data sangat sedikit. Metode machine learning yang digunakan untuk membangun model adalah Support Vector Machine (SVM) dengan metode ekstraksi fitur reliefF. Berdasarkan penelitian yang dilakukan, akurasi sistem dengan metode SVM tanpa seleksi fitur ReliefF adalah 83%. Sedangkan akurasi yang diperoleh sistem dengan seleksi fitur ReliefF mencapai 93% dengan penggunaan 85% fitur dari total keseluruhan fitur.

show abstract

“…So the author conducts Text Mining Analysis on the data that the author has scraped on twitter using Netlytic with the keywords #Joko Widodo and #pemerintahanjokowi taken between August-October 2022, so that the data can be used to identify trends and patterns in public views on government policies and actions. This can be done by collecting and analyzing news from social media posts and other sources of information about the government using Text Mining techniques [6] to identify the sentiment of the public towards president Joko Widodo's administration. Text Mining can also identify key issues of public concern, such as economic development, corruption, or social welfare.…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis of Public Responses on Indonesia Government Using Naïve Bayes and Support Vector Machine

Setyawan,

Azizah,

Pradani

2023

EIST

View full text Add to dashboard Cite

Many people are interested in knowing how the public views President Joko Widodo's administration. Text Mining analysis can be one way to collect and analyze text data about Joko Widodo's administration and extract relevant information from the data. Data was obtained by collecting tweet data about Joko Widodo's government in 2022 on Twitter using Netlyitic. Then the Text Mining analysis of Joko Widodo's government was carried out using the Navie Bayes (NVB) classification and Support Vector Machine (SVM). This classification can be used to predict sentiment or public views of the government based on the tweets collected. Based on a case study of the classification results of President Joko Widodo using Naive Bayesian classification, we obtained a precision value of 79%, a recall value of 91% and a precision value of 82%. And by using SVM, we get 85% precision, 95% recall, and 83% precision. Due to the high accuracy, recall, and precision, it can be said that SVM classification is more accurate than NVB.

show abstract

Text Mining and Support Vector Machine for Sentiment Analysis of Tourist Reviews in Bangkalan Regency

Cited by 9 publications

References 1 publication

Applying bag of words approach to determine remote sensing technology acceptance among smallholder plantations

Applying bag of words approach to determine remote sensing technology acceptance among smallholder plantations

Pendekatan Data Science untuk Mengukur Empati Masyarakat terhadap Pandemi Menggunakan Analisis Sentimen dan Seleksi Fitur

Sentiment Analysis of Public Responses on Indonesia Government Using Naïve Bayes and Support Vector Machine

Contact Info

Product

Resources

About