基于Twitter数据的谷物价格不稳定情绪分析

Dedy Sugiarto, Reyhan Dwi Putra, Wahyu Hidayat, Ema Utami, Ainul Yaqin
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引用次数: 1

摘要

-舆论,无论是积极的,消极的,还是中立的,对于特定的政策或社会现象,都是有价值的,可以通过情感分析的方法来分析。本研究的案例是2021年初至中期粮食价格的下降。本研究旨在确定与谷物价格关键字相关时出现的情绪极性百分比,并使用Naïve贝叶斯方法确定情绪类预测的准确性水平。调查结果显示,“负面”占46.30%,“中性”占32.70%,“正面”占20.99%。wordcloud的结果还显示,推特用户将粮食价格问题与大米进口、政府角色和化肥联系起来。分类结果显示,准确率达到了67.32%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analisis Sentimen Ketidakstabilan Harga Gabah Berbasis Data Twitter
- Public opinion, whether positive, negative, or neutral, regarding a particular policy or phenomenon in society, is a valuable thing to analyze through a method known as sentiment analysis. The case in this study is the decline in grain prices in early to mid-2021. This study aims to determine the percentage of sentiment polarity that appears when associated with the keyword price of grain and determine the level of accuracy of sentiment class predictions using the Naïve Bayes method. The results showed that the largest percentage of sentiment was negative as much as 46.30%, neutral 32.70% and positive as much as 20.99%. The results of the wordcloud also show that twitter users link the issue of grain prices to rice imports, the role of the government and fertilizers. The results of the classification show a fairly good accuracy value of 67.32%.
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