Tax Complaints Classification on Twitter Using Text Mining

The majority of the current research on sentiment analysis, which covers topics like political reviews, movie reviews, and product reviews, has developed quickly. The classification and clustering stage of sentiment analysis research involves a number of subjects. Some of them cover text classification comparison research and algorithm performance optimization. An intricate issue in sentiment analysis research is dealing with unstructured or semi-structured data. The sentiment analysis procedure and improving the efficacy of the classifier’s algorithm are both hampered by unstructured data. In order to manage unstructured data successfully and provide accurate and relevant information, unique strategies are required. The proposed classification model performance evaluation using Support Vector Machine, Naive Bayes, K-Nearest Neighbor, and Decision Tree is specifically covered in this paper. According to the study’s findings, SVM has an accuracy rate of 96% and Naive Bayes is 86%. While the decision tree’s gain accuracy is 78 percent and the kNN classification model’s gain accuracy is 78 percent, respectively. The test results demonstrate that SVM is superior to other classification models in terms of accuracy performance.

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“…The training method consists of estimating the relative frequencies of 𝑃 (𝑐) and 𝑃 (𝑓 | 𝑐) using add-one smoothing [22,23].…”

Section: Sentiment Classification Aproachmentioning

confidence: 99%

Performance Evaluation of Classification Algorithm for Movie Review Sentiment Analysis

Sutriawan¹,

Andono²,

Muljono³

et al. 2023

IJC

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“…Meanwhile, P. Dellia and A. Tjahyanto [11] conducted tax complaints classification on Twitter using Naive Bayes, SVM and decision tree. The total tweets used were 1,001 tweets with 758 tweets labelled as no-complaints and 243 tweets labelled as complaints.…”

Section: Literature Reviewmentioning

confidence: 99%

Sentiment Analysis Towards Kartu Prakerja Using Text Mining with Support Vector Machine and Radial Basis Function Kernel

Ardhani

Chamidah

Saifudin

2021

JISEBI

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Background: The introduction of Kartu Prakerja (Pre-employment Card) Programme, henceforth KPP, which was claimed to have launched in order to improve the quality of workforce, spurred controversy among members of the public. The discussion covered the amount of budget, the training materials and the operations brought out various reactions. Opinions could be largely divided into groups: the positive and the negative sentiments.Objective: This research aims to propose an automated sentiment analysis that focuses on KPP. The findings are expected to be useful in evaluating the services and facilities provided.Methods: In the sentiment analysis, Support Vector Machine (SVM) in text mining was used with Radial Basis Function (RBF) kernel. The data consisted of 500 tweets from July to October 2020, which were divided into two sets: 80% data for training and 20% data for testing with five-fold cross validation.Results: The results of descriptive analysis show that from the total 500 tweets, 60% were negative sentiments and 40% were positive sentiments. The classification in the testing data show that the average accuracy, sensitivity, specificity, negative sentiment prediction and positive sentiment prediction values were 85.20%; 91.68%; 75.75%; 85.03%; and 86.04%, respectively.Conclusion: The classification results show that SVM with RBF kernel performs well in the opinion classification. This method can be used to understand similar sentiment analysis in the future. In KPP case, the findings can inform the stakeholders to improve the programmes in the future. Keywords: Kartu Prakerja, Sentiment Analysis, Support Vector Machine, Text Mining, Radial Basis Function

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“…The emergence of unlimited textual information, so that the public can easily submit complaints that can contain any information, causes difficulties in handling complaints [1].…”

Section: Introductionmentioning

confidence: 99%

Classification of Public Complaints Basedon Text Mining Using Modified K-Nearest Neighbor, Naïve Bayes and C4.5 Algorithm

Bahri¹,

Utami²,

Nasiri³

2022

ccitj

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To improve public services, accuracy and acceleration are needed in classifying the types of complaints so that complaints can immediately get a response from the relevant regional apparatus. This public complaint data is in text form and is not balanced in each category of regional apparatus, so we contribute to research to compare the performance of different text mining-based classification algorithms. In addition, we also tested the resampling method to overcome imbalanced data. In the final stage, testing is carried out using a multiclass confusion matrix table to show accuracy, precision, recall, and f1-score. The test results show the highest value in the Naïve Bayes algorithm with the ComplementNB model without resampling data, which is 89.58% accuracy, 86.72% precision, 82.40% recall, 84.09% f1-score. However, all scores decreased when combined with SMOTE resampling of 83.66% accuracy, 67.79% precision, 80.35% recall, 71.68% f1-score. ComplementNB can be an alternative model in the classification of public complaints with imbalanced datasets

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Tax Complaints Classification on Twitter Using Text Mining

Cited by 7 publications

References 17 publications

Performance Evaluation of Classification Algorithm for Movie Review Sentiment Analysis

Performance Evaluation of Classification Algorithm for Movie Review Sentiment Analysis

Sentiment Analysis Towards Kartu Prakerja Using Text Mining with Support Vector Machine and Radial Basis Function Kernel

Classification of Public Complaints Basedon Text Mining Using Modified K-Nearest Neighbor, Naïve Bayes and C4.5 Algorithm

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