Sentiment analysis on Twitter posts: An analysis of positive or negative opinion on GoJek

Windasari, Ike Pertiwi; Uzzi, Fajar Nurul; Satoto, Kodrat Iman

doi:10.1109/icitacee.2017.8257715

Cited by 38 publications

(26 citation statements)

References 2 publications

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“…Melalui Twitter, pengguna dapat saling berdiskusi mengenai kritik, saran ataupun kepuasan mereka terhadap layanan Indihome [3]. Perbedaan Twitter dari media sosial yang lain adalah Twitter memungkinkan tiap pengguna dapat melacak tweet pengguna lain tanpa persetujuan dari perngguna tersebut [4].…”

Section: Pendahuluanunclassified

Analisis Sentimen Terhadap Layanan Indihome Berdasarkan Twitter Dengan Metode Klasifikasi Support Vector Machine (SVM)

Tineges

Triayudi

Sholihati

2020

mib

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In the year 2018, 18.9% of the population in Indonesia mentioned that the main reason for their use of the Internet is social media. One of the social media with an active user of 6.43 million users is Twitter. Based on the surge of information published via Twitter, it is possible that such information may contain the user's opinions on an object, such objects may be events around the community such as a product or service. This makes the company use Twitter as a medium to disseminate information. An example is an Internet Service Provider (ISP) such as Indihome. Through Twitter, users can discuss each other's complaints or satisfaction with Indihome's services. It takes a method of sentiment analysis to understand whether the textual data includes negative opinions or positive opinions. Thus, the authors use the Support Vector Machine (SVM) method in sentiment analysis on the opinions of the Indihome service user on Twitter, with the aim of obtaining a sentiment classification model using SVM, and to know how much accuracy the SVM method generates, which is applied to sentiment analysis, and to see how satisfied the Indihome service users are based on Twitter. After testing with SVM method The result is accuracy 87%, precision 86%, recall 95%, error rate 13%, and F1-score 90%

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Section: Pendahuluanunclassified

Analisis Sentimen Terhadap Layanan Indihome Berdasarkan Twitter Dengan Metode Klasifikasi Support Vector Machine (SVM)

Tineges

Triayudi

Sholihati

2020

mib

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“…The other research of sentiment analysis in the online transportation industry in Indonesia was conducted by Windasari et.al [4]. The data of online transportation were extracted from Twitter.…”

Section: Related Workmentioning

confidence: 99%

“…Fiarni et.al conducted sentiment analysis of online stores in Indonesia, with social media as the main data sources [3]. The same is for research [4] [5], social media is used as the data. Research of Muthia [6] focused on the review data from a website of the restaurant, while research [7] focused on tourism sites.…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis in the Sales Review of Indonesian Marketplace by Utilizing Support Vector Machine

Lutfi

Permanasari

Fauziati

2018

JISEBI

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The online store is changing people’s shopping behavior. Despite the fact, the potential customer’s distrust in the quality of products and service is one of the online store’s weaknesses. A review is provided by the online stores to overcome this weakness. Customers often write a review using languages that are not well structured. Sentiment analysis is used to extract the polarity of the unstructured texts. This research attempted to do a sentiment analysis in the sales review. Sentiment analysis in sales reviews can be used as a tool to evaluate the sales. This research intends to conduct a sentiment analysis in the sales review of Indonesian marketplace by utilizing Support Vector Machine and Naive Bayes. The reviews of the data are gathered from one of Indonesian marketplace, Bukalapak. The data are classified into positive or negative class. TF-IDF is used to feature extraction. The experiment shows that Support Vector Machine with linear kernel provides higher accuracy than Naive Bayes. Support Vector Machine shows the highest accuracy average. The generated accuracy is 93.65%. This approach of sentiment analysis in sales review can be used as the base of intelligent sales evaluation for online stores in the future.

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“…In previous studies, according to Windasari et al [3], Support Vector Machine method is a supervised learning method that more optimal than the Naïve Bayes method.…”

Section: Introductionmentioning

confidence: 99%

“…Multiclass SVM OAA approach in the research of Hejazi et al, Pratama et al [5], and Mustakim et al [6] has a better accuracy value than Multiclass SVM OAO. Based on the discussion above, this paper will classify Twitter data user sentences into positive, neutral and negative using the Multiclass Support Vector Machine (SVM) One Against All (OAA) classification using a radial basis function kernel with five TF-IDF feature weighting approaches namely unigram [3], bigram [4], trigram [7], unigram + bigram, and word cloud [8] to map people's sentiment into positive, negative, or neutral. From the combination of five different features will be found the best features for data classification.…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis on KAI Twitter Post Using Multiclass Support Vector Machine (SVM)

Fitriana

Sibaroni

2020

RESTI

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Information in form of unstructured texts is increasing and becoming commonplace for its existence on the internet. This information is easily found and utilized by business people or companies through social media. One of them is Twitter. Twitter is ranked 6th as a social media that is widely accessed today. The use of Twitter has the disadvantage of unstructured and large data. Consequently, it is difficult for business people or companies to know opinion towards service with limited resources. To Make it easier for businesses know the public's sentiment for better service in the future, public sentiment on Twitter needs to be classified as positive, neutral, and negative. The Multiclass Support Vector Machine (SVM) method is a supervised learning classification method that handles three classes classification. This paper uses One Against All (OAA) approach as a method to determine the class. This paper contains the results of classifying OAA Multiclass SVM methods with five different weighting features unigram, bigram, trigram, unigram+ bigram, and word cloud for analyzing tweet data, finding the best accuracy and important feature when processed with large data. The highest accuracy is the unigram TF-IDF model combined with the OAA Multiclass SVM with gamma 0.7 is 80.59.

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Sentiment analysis on Twitter posts: An analysis of positive or negative opinion on GoJek

Cited by 38 publications

References 2 publications

Analisis Sentimen Terhadap Layanan Indihome Berdasarkan Twitter Dengan Metode Klasifikasi Support Vector Machine (SVM)

Analisis Sentimen Terhadap Layanan Indihome Berdasarkan Twitter Dengan Metode Klasifikasi Support Vector Machine (SVM)

Sentiment Analysis in the Sales Review of Indonesian Marketplace by Utilizing Support Vector Machine

Sentiment Analysis on KAI Twitter Post Using Multiclass Support Vector Machine (SVM)

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