地球资源数据云——数据资源详情
职位空缺推文
该数据集《Job Vacancy Tweets》主要用于监督学习任务，数据形态以文本为主，应用场景偏向文本内容分析。 题目说明：NLP Dataset for Beginners 任务类型：文本监督学习。 建议流程：先做文本清洗与分词，再比较 TF - IDF+线性模型 与 预训练语言模型。 评估建议：使用分层切分或交叉验证，优先关注 F1、Recall、AUC 等分类指标。 可用文件：Job_Tweets.csv。 This dataset contains 50,000 tweets related to job vacancies and hiring, extracted using the keywords 'Job Vacancy,' 'We are Hiring,' and 'We're Hiring'. The tweets were collected between January 1, 2019, and April 10, 2023, with the help of snscrape library of Python and are provided in a CSV format. The purpose behind this dataset To explore text pre - processing and test NLP skills Draw interesting insights on Job Market from Job Postings. Analyse company/role requirements if possible
摘要概览
该数据集《Job Vacancy Tweets》主要用于监督学习任务，数据形态以文本为主，应用场景偏向文本内容分析。 题目说明：NLP Dataset for Beginners
任务类型：文本监督学习。
建议流程：先做文本清洗与分词，再比较 TF - IDF+线性模型 与 预训练语言模型。
评估建议：使用分层切分或交叉验证，优先关注 F1、Recall、AUC 等分类指标。
可用文件：Job_Tweets.csv。
This dataset contains 50,000 tweets related to job vacancies and hiring, extracted using the keywords 'Job Vacancy,' 'We are Hiring,' and 'We're Hiring'. The tweets were collected between January 1, 2019, and April 10, 2023, with the help of snscrape library of Python and are provided in a CSV format.
The purpose behind this dataset
To explore text pre - processing and test NLP skills
Draw interesting insights on Job Market from Job Postings.
Analyse company/role requirements if possible