Classification and Sentiment Analysis on Tweets of the Ministry of Health Republic of Indonesia

Angdresey, Apriandy; Kairupan, Indah; Emor, Kenshin Geraldy

doi:10.1109/icic56845.2022.10007008

Cited by 1 publication

(1 citation statement)

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“…This not only benefits the Indonesian Ministry of Health by simplifying the study of public opinions but also improves public accessibility by enabling people to more readily examine classified material. Part of this research has been presented in (Angdresey et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

An Extreme Gradient Boosting Approach for Classification and Sentiment Analysis

Kairupan,

Angdresey,

Arif

2023

ajtm

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Since 2020, when the coronavirus epidemic was at its peak, the Indonesian Ministry of Health's social media accounts have been constantly followed by a big number of individuals. The Indonesian Ministry of Health account is a fantastic resource for social media users, particularly Twitter users. The Republic of Indonesia's Ministry of Health's Twitter account publishes a wide range of content at random. As a result, it is usually difficult for Twitter users to determine the type of information provided by the Ministry of Health of the Republic of Indonesia. The positive and negative responses of Twitter users to material released by the Indonesian Ministry of Health's Twitter account are frequently noted. The decision tree algorithm is tree-based, similar to the extreme gradient boosting method (XGBoost). The extreme gradient boosting approach has been successfully implemented with high performance in the classification process. This classification is separated into two primary categories: general and essential information categorization and sentiment analysis, which is classified into three classes: positive, neutral, and negative. Both the classification work and the sentiment analysis produced outstanding accuracy levels. Based on 2243 tweets, an accuracy rate of 89.35% has been achieved for classification, supported by a precision of 88.76% and a recall value of 88.58% when using 80 data training and 20 data testing. Similarly, the maximum accuracy in sentiment analysis was achieved utilizing the same 80-20 data partitioning, with a 91.22% accuracy rate. Using 304 comments data, accuracy was calculated to be 89.17% and recall was calculated to be 89.06%. It's worth noting that an 80-20 split for training and testing consistently produced the best results for both the sentiment analysis and classification tasks.

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Section: Introductionmentioning

confidence: 99%