Sentiment Analysis of The Public Perspective Electric Cars in Indonesia Using Support Vector Machine Algorithm

Alhari, Muhammad Ilham; Pratiwi, Oktariani Nurul; Lubis, Muharman

doi:10.1109/icsintesa56431.2022.10041604

Cited by 2 publications

(3 citation statements)

References 21 publications

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“…Furthermore, when considering the years in which the research was conducted, the category of methods most frequently employed over the last two years is Neural Network/Deep Learning, found in 7 literature [8], [9], [10], [13], [18], [20], [22] followed by the Classic category present in 6 literature [8], [10], [11], [12], [13], [22]. The methods used in sentiment analysis found in the 15 literature is illustrated in Table 4.…”

Section: Methodsmentioning

confidence: 99%

“…Out of the 15 analyzed literature, 11 of them [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18] utilized the social media platform Twitter for sentiment analysis, 1 literature [19] used Weibo, which is a commonly used social media platform in China, and 2 literature [20], [21] used government websites. Furthermore, there is one literature [22] that utilized three social media platforms, namely Twitter, Reddit, and Youtube.…”

Section: Media Sosialmentioning

confidence: 99%

“…Data Preprocessing Technique [8], [10], [11], [14] Tokenization [8], [10], [11], [15], [20], [22] Stop-word removal [8], [10], [13]- [15], [22] Stemming [8] Feature extraction [9]- [13], [17]- [20], [22] Cleaning the punctuation, url, and emojis [10], [15] Case Folding [10] Bag of words [10] Labeling data…”

Section: Table 3 Data Prepocessing Technique Literaturementioning

confidence: 99%

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Sentiment Analysis of Air Pollution on Social Media: Systematic Literature Review

Permana,

Gofur,

Budi

et al. 2024

SISTEMASI

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The need for a healthy and pollution-free environment is the basis of the problem that this study examines. Social media has become an integral aspect of daily existence for the majority engaged in the digital realm. It enables individuals from various backgrounds to utilize these platforms to stay updated on the latest information, such as the current state of pollution in Jakarta. This research explores the attitudes of social media users regarding their perspectives on air pollution in Jakarta. The method used includes conducting a Systematic Literature Review of academic papers released from 2020 to 2023. The results of this research can unveil the types of social media platforms utilized, the quantity of datasets, the procedures for data collection, data preprocessing techniques, and the commonly employed methods in sentiment analysis studies concerning the subject of air pollution.

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Section: Methodsmentioning

confidence: 99%

Section: Media Sosialmentioning

confidence: 99%

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Sentiment Analysis of Air Pollution on Social Media: Systematic Literature Review

Permana,

Gofur,

Budi

et al. 2024

SISTEMASI

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Sentiment Analysis on Social Media: Investigating Users' Perceptions of MRT and LRT Transportation Services

Muhamud Fozi,

Zainuddin,

Naziron

2024

IMBR

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Providing excellent public transportation in response to the passenger’s complaints and recommendations results in long-term improvements to the service. This study investigates public perceptions of the MRT and LRT rail transportation services within the Klang Valley Integrated Transit System, operated by Rapid KL, through sentiment analysis in X. With 4.4 million users in Malaysia as of January 2022, X (previously Twitter) media social serves as a significant platform for public discourse. However, analyzing these perceptions poses challenges due to the limited platforms for analysis, and seeking from X is even more challenging due to the unstructured and noisy nature of the tweets. Therefore, this study aims to develop a sentiment analysis model that organizes tweets into structured data, utilizing machine learning techniques for sentiment classification into positive, neutral, and negative categories. Following the model implementation, the data are collected, translated, cleaned, labeled, analyzed, and classified using a Support Vector Machine before being deployed in a web system for ease of access. Analysis results revealed that user sentiment is predominantly neutral, with a significant focus on MRT services and topic finding related to scheduling. The model scored good accuracy 80% without a kernel and 84% with a Linear kernel, with evaluation metrics demonstrating strong performance on all three sentiment categories. Future enhancements will include label refining and applying more hyperparameter tuning to improve analysis accuracy.

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Sentiment Analysis of The Public Perspective Electric Cars in Indonesia Using Support Vector Machine Algorithm

Cited by 2 publications

References 21 publications

Sentiment Analysis of Air Pollution on Social Media: Systematic Literature Review

Sentiment Analysis of Air Pollution on Social Media: Systematic Literature Review

Sentiment Analysis on Social Media: Investigating Users' Perceptions of MRT and LRT Transportation Services

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