情感分析库的比较分析
Comparative Analysis of Libraries for the Sentimental Analysis
AI总结：
本研究比较了NLTK、TextBlob、Vader、Transformers和Tidytext等库及DT、SVM、NB、KNN四种机器学习模型在社交网络情感分析中的性能，发现BERT transformer方法以0.973的准确率表现最佳。
摘要：
本研究的主要目标是提供使用机器学习方法的库的比较比较。自然语言处理（NLP）专家对文本变化的情感分析（SA）越来越感兴趣。采用NLP文本分析技术的目的是识别和分类与推特用户话语相关的情感。在本研究中，还探讨了SA的问题以及所使用的库。提供了多种协作方法来分类情感极性。根据最近的研究，朴素贝叶斯分类器、决策树分类器、最大熵分类器、Sklearn分类器、Sklearn分类器MultinomialNB以及其他联合学习算法非常有效。在项目中将使用五个Python和R库NLTK、TextBlob、Vader、Transformers（GPT和BERT预训练）和Tidytext来应用情感分析技术。还将使用四种机器学习模型决策树（DT）、支持向量机（SVM）、朴素贝叶斯（NB）和K近邻（KNN）。为了评估SA库在社交网络环境中的运行情况，还进行了比较研究。本实验中评估最佳算法的指标为精确率、召回率和F1分数，每种方法使用单一数据集。我们得出结论，推荐使用BERT transformer方法进行情感分析，其准确率为0.973。
英文摘要：
This study is main goal is to provide a comparative comparison of libraries using machine learning methods. Experts in natural language processing (NLP) are becoming more and more interested in sentiment analysis (SA) of text changes. The objective of employing NLP text analysis techniques is to recognize and categorize feelings related to twitter users utterances. In this examination, issues with SA and the libraries utilized are also looked at. provides a number of cooperative methods to classify emotional polarity. The Naive Bayes Classifier, Decision Tree Classifier, Maxent Classifier, Sklearn Classifier, Sklearn Classifier MultinomialNB, and other conjoint learning algorithms, according to recent research, are very effective. In the project will use Five Python and R libraries NLTK, TextBlob, Vader, Transformers (GPT and BERT pretrained), and Tidytext will be used in the study to apply sentiment analysis techniques. Four machine learning models Tree of Decisions (DT), Support Vector Machine (SVM), Naive Bayes (NB), and K-Nearest Neighbor (KNN) will also be used. To evaluate how well libraries for SA operate in the social network environment, comparative study was also carried out. The measures to assess the best algorithms in this experiment, which used a single data set for each method, were precision, recall, and F1 score. We conclude that the BERT transformer method with an Accuracy: 0.973 is recommended for sentiment analysis.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
