基于Naïve贝叶斯的婆罗浮屠票情绪数据驱动分析。

Dedi Kundana, Chairani
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引用次数: 0

摘要

最近社交媒体的增长具有巨大的影响力,在数字时代人们生活的各个方面发挥着重要作用。Twitter是一个在印度尼西亚广泛使用的社交媒体网络。Twitter用户可以参与多种活动,例如与个人和团体交流,撰写日常活动,促进业务,争论和表达关于讨论主题的想法。2022年6月初,提高婆罗浮屠寺的入场费成为人们关注的问题之一,在现实世界和包括推特在内的其他社交媒体平台上引发了很多讨论。提高婆罗浮屠寺门票价格的计划在社区中引起了各种赞成和反对。本研究分析公众对婆罗浮屠寺票价计划上调的情绪。Twitter数据的情感分析可以使用分类算法来实现。在情感分析研究中广泛使用的分类算法有朴素贝叶斯(NB)和决策树(DT)。之所以选择朴素贝叶斯和决策树,是因为该算法是目前最常用的文本数据分类处理算法;该工艺简单、高效、性能好。这项研究的数据来源来自Twitter等社交媒体网站。与产生100%测试百分比的决策树相比,基于对测试结果的评估,朴素贝叶斯方法的准确性产生了最高的准确性数字。同时,决策树方法的准确率测试得到了35.97%的测试准确率值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Data Driven Analysis of Borobudur Ticket Sentiment Using Naïve Bayes.
 The recent growth of social media is hugely influential and plays a significant role in various aspects of people's lives in the digital era. Twitter is a social media network that is widely used in Indonesia. Twitter users can engage in multiple activities, such as communicating with individuals and groups, writing daily activities, promoting businesses, arguing, and expressing ideas about a topic of discussion. At the beginning of June 2022, raising the entrance charge for Borobudur Temple became one of the concerns that caused a lot of conversation in the real world and on other social media platforms, including Twitter. The plan to increase the price of entrance tickets to Borobudur Temple has drawn various pro and con reactions in the community. This study analyzes public sentiment toward the planned increase in ticket prices for Borobudur Temple. Sentiment analysis of Twitter data can be implemented using a classification algorithm. The classification algorithms widely used in sentiment analysis research are Nave Bayes (NB) and Decision Tree (DT). The reason for choosing Nave Bayes and Decision Tree is because this algorithm is the most popular algorithm used to process text data classification; the process is simple, efficient, and performs well. This study's dataset source was taken from social media sites like Twitter. In comparison to the Decision Tree, which generates a test percentage of 100%, the accuracy of the Naive Bayes approach, based on the evaluation of the test results, produces the highest accuracy number. At the same time, the Decision Tree method's accuracy test yields a test accuracy value of 35.97%.
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