[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-80084":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":8,"htmlUrl":8,"language":9,"languages":8,"totalLinesOfCode":8,"stars":10,"forks":11,"watchers":10,"openIssues":11,"contributorsCount":11,"subscribersCount":11,"size":11,"stars1d":11,"stars7d":11,"stars30d":11,"stars90d":11,"forks30d":11,"starsTrendScore":11,"compositeScore":12,"rankGlobal":8,"rankLanguage":8,"license":8,"archived":13,"fork":13,"defaultBranch":14,"hasWiki":15,"hasPages":13,"topics":16,"createdAt":8,"pushedAt":8,"updatedAt":17,"readmeContent":18,"aiSummary":19,"trendingCount":11,"starSnapshotCount":11,"syncStatus":20,"lastSyncTime":21,"discoverSource":22},80084,"Sentiment-Analysis","varshini11ravi\u002FSentiment-Analysis","varshini11ravi",null,"Python",59,0,34,false,"main",true,[],"2026-06-12 04:01:26","# Sentiment-Analysis","该项目是一个基于Python的情感分析工具，旨在自动识别和提取文本中的情绪倾向。它通过自然语言处理技术对输入的文本数据进行处理，能够判断出积极、消极或中立的情绪状态。项目采用了先进的机器学习算法来提高情感分类的准确性，并且支持用户自定义训练模型以适应特定领域的语料库。非常适合用于社交媒体监控、产品评论分析以及任何需要快速了解大量文本内容情感色彩的应用场景。",2,"2026-06-11 03:59:12","CREATED_QUERY"]