摘要
由一名新闻记者-机器人与机器学习每日新闻的工作人员新闻编辑-调查人员发布了一份新的报告。根据NewsRx编辑在艾哈迈德·达兰大学的To News报道，“Twitter是一个社交媒体平台，在数字世界中非常重要。”我们的新闻记者从Ahmad Dahlan University的研究中获得了一句话：“快速的沟通和互动使Twitter成为情感分析的重要信息中心。本研究的目的是利用基于1276条推文的非线性SVM算法，对印度尼西亚市场存在的正面和负面信息进行分类。本研究包括数据预处理阶段，最后一个过程GridSearchCV,将数据划分为三个场景:80%的训练数据和20%的测试数据,50%的训练数据和50%的测试数据场景,20%的训练数据和80%的测试数据场景。结合交叉验证和非线性SVM参数，使用混淆矩阵进行模型评估。该场景产生的BES T SVM模型是80%的训练和20%的测试数据，超参数Gamma=100和C=0.01，达到89%的准确度。在从未见过的数据上测试时，准确度提高到90%，F1得分为91%，精确度为88%，负面情绪的Recal L为95%。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Investigators publish new report on . According t o news reporting out of Ahmad Dahlan University by NewsRx editors, research stat ed, “Twitter is a social media platform that is very important in the digital wo rld.” Our news reporters obtained a quote from the research from Ahmad Dahlan Universi ty: “Fast communication and interaction make Twitter a vital information center in sentiment analysis. The purpose of this research is to classify public opinio n about the presence of marketplaces in Indonesia, both positive and negative se ntiments, using a Non-linear SVM algorithm based on 1276 tweets. This research i nvolves the stages of data pre-processing, labeling, feature extraction using TF -IDF, and data division into three scenarios: 80% training data an d 20% test data, 50% training data and 50% test data scenario, and 20 % training data and 80% te st data scenario. The last process, GridSearchCV, combines cross-validation and non-linear SVM parameters for model evaluation using a confusion matrix. The bes t SVM model resulting from the scenario was 80% training and 20% test data, with hyperparameters Gamma = 100 and C = 0.01, achieving 89% accuracy. When tested on never-before-seen data, the accuracy increased to 90 % , with an f1-score of 91%, precision of 88%, and recal l of 95% on negative sentiments.”
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