Sentiment Analysis of Customers on Utilizing Online Motorcycle Taxi Service at Twitter with the Support Vector Machine

Jaman, Jajam Haerul; Abdulrohman, Rasdi

doi:10.1109/icecos47637.2019.8984483

Cited by 7 publications

(6 citation statements)

References 4 publications

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“…The second in [7] The author presented also showed that SVM provides a fairly good level of accuracy from 1183 data to 90% training data and 10% data testing, the result is linear use 0.8, RBF 0.78, Sigmoid 0.8, and polynomial 0.77.…”

Section: Related Workmentioning

confidence: 94%

“…In the early stages of this paper is limited to the two largest online transportation companies in Indonesia namely GoJek Indonesia and GrabId, following the retrieval of data taken from [7] in April 2019 then continued the process of crawling data using twitter APIs in April to June 2020 where this era is referred to as the era of COVID-19 pandemic.…”

Section: Data Collecting / Methods and Techniquementioning

confidence: 99%

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Sentiment Analysis on Utilizing Online Transportation of Indonesian Customers Using Tweets in the Normal Era and the Pandemic Covid-19 Era with Support Vector Machine

Jaman¹,

Abdulrohman²,

Suharso³

et al. 2020

Adv. sci. technol. eng. syst. j.

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Online transportation in Indonesia is a new trend of transportation that is currently used among the lower to upper society. The change in behavior began in 2011 and is growing to this day, The comments that are growing on social media are very important for the online transportation company the negative comments lower the level of users while the good comments increase the users' level. Thus, the comments influence the overall trust of their customers. Among social media, Twitter is a place where many people convey feelings of pleasure and displeasure timely, especially at a time when the COVID-19 pandemic is becoming a serious outbreak. Through these "tweets," many customers express their experience with the service. In this paper, we aim to analyze the experience of online transportation consumers using Support Vector Machine. The data were taken in two periods, i.e. April 2019 ("the normal era") and June 2020 ("the COVID-19 pandemic outbreak. Class logging is done based on 3 categories namely positive, negative and normal, while in mining the data we labeled with the keywords @grabID and @gojekindonesia, 1618 data were obtained with a ratio of 1183 is normal era data and 435 data in the era of COVID-19 pandemic, The highest accuracy results occurred in the normal era with a ratio of 10% as test data and 90% as its training data on linear and sigmoid kernels of 0.8060 while the COVID-19 era only got the highest accuracy of 0.59 in linear kernels with a ratio of 60:40. This is a sign that the COVID-19 pandemic does not contribute to decreasing trust in the service.

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Section: Related Workmentioning

confidence: 94%

Section: Data Collecting / Methods and Techniquementioning

confidence: 99%

Sentiment Analysis on Utilizing Online Transportation of Indonesian Customers Using Tweets in the Normal Era and the Pandemic Covid-19 Era with Support Vector Machine

Jaman¹,

Abdulrohman²,

Suharso³

et al. 2020

Adv. sci. technol. eng. syst. j.

Self Cite

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show abstract

“…In this process, entities and unnecessary information are removed. Preprocessing is done through several stages: cleaning, case folding, tokenizing, normalization, stopwords, and stemming [14]. Preprocessing aims to convert raw data into more structured data to be recognizable to the machine [15], [16].…”

Section: B Preprocessingmentioning

confidence: 99%

Sentiment Analysis of Covid-19 Vaccination using Support Vector Machine in Indonesia

Rahardi¹,

Aminuddin²,

Abdulloh³

et al. 2022

IJACSA

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Along with the development of the Covid-19 pandemic, many responses and news were shared through social media. The new Covid-19 vaccination promoted by the government has raised pros and cons from the public. Public resistance to covid-19 vaccination will lead to a higher fatality rate. This study carried out sentiment analysis about the Covid-19 vaccine using the Support Vector Machine (SVM). This research aims to study the public response to the acceptance of the vaccination program. The research result can be used to determine the direction of government policy. Data collection was taken via Twitter in the year 2021. The data then undergoes the preprocessing methods. Afterward, the data is processed using SVM classification. Finally, the result is evaluated by a confusion matrix. The experimental result shows that SVM produces 56.80% positive, 33.75% neutral, and 9.45% negative. The highest model accuracy was obtained by RBF kernel of 92%, linear and polynomial kernels obtained 90% accuracy, and sigmoid kernel obtained 89% accuracy.

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“…The four kernels used in the classification process are linear, RBF, sigmoid and polynomial. The highest accuracy results were obtained in the scenario of 90% of the train data and 10% of the test data using linear and sigmoid kernels of more than 80% [5].…”

Section: Introductionmentioning

confidence: 98%

“…In addition, based on previous studies, the SVM algorithm can produce the highest confusion matrix value compared to other classification algorithms. Proven by the result of research by Jaman et al [5] that using SVM can produce a high accuracy of more than 80%. The topics discussed can be modeled with the Latent Dirichlet Allocation (LDA) method.…”

Section: Introductionmentioning

confidence: 99%

Sentiment and Discussion Topic Analysis on Social Media Group using Support Vector Machine

Adityani

Richasdy

Astuti

2022

mib

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The growth of social media in this modern era is increasingly rapid, where people are very active digitally interacting with each other. People who have a common interest or simply like to be in a community often gather in an online group, especially on Facebook. Alumni of Telkom University are no exception, who are also actively discussing and sharing information in Telkom University Alumni Forum Facebook group (FAST). By using their status from that group, sentiment and topic discussion analysis can be performed to determine whether the polarity is positive, neutral, or negative. In Addition, topic modeling extracts what topics are often discussed in the group. In this research, sentiment analysis was performed using the Support Vector Machine (SVM) method. Also, the classification process involved TF-IDF for word weighting and confusion matrix as performance measurement. Several testing scenarios were carried out to get the best accuracy value. Based on the tests performed on the preprocessing technique and feature extraction n-gram addition, the highest accuracy value obtained is 80.56%. The result indicates that the best performance is obtained by combining preprocessing techniques without the stopword removal process and feature extraction unigram. Moreover, the topics discussed based on topic modeling results were related to telecommunication and Telkom, Indonesia, alumni, and FAST.

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Sentiment Analysis of Customers on Utilizing Online Motorcycle Taxi Service at Twitter with the Support Vector Machine

Cited by 7 publications

References 4 publications

Sentiment Analysis on Utilizing Online Transportation of Indonesian Customers Using Tweets in the Normal Era and the Pandemic Covid-19 Era with Support Vector Machine

Sentiment Analysis on Utilizing Online Transportation of Indonesian Customers Using Tweets in the Normal Era and the Pandemic Covid-19 Era with Support Vector Machine

Sentiment Analysis of Covid-19 Vaccination using Support Vector Machine in Indonesia

Sentiment and Discussion Topic Analysis on Social Media Group using Support Vector Machine

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