地球资源数据云——数据资源详情
nlp 餐厅评论
该数据集《Restaurant reviews for nlp》主要用于多分类任务，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：Restaurants reviews data set, well know for nlp beginners 任务类型：文本多分类。 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：未检测到标准 CSV，可优先查看目录中的索引或说明文件。 (1) analyze negative reviews and feedback from guests of fast food restaurant; (2) get an idea about how various fast food restaurants do in different cities (what are the different defects in services, food, etc); (3) develop NLP classification models that can classify negative reviews for restaurant managers, help the manager save time on absorbing information, help restaurants improve the business. (4) try various ML technics, deep learning, xgboost to build NLP models, evaluate models.
摘要概览
该数据集《Restaurant reviews for nlp》主要用于多分类任务，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：Restaurants reviews data set, well know for nlp beginners
任务类型：文本多分类。
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：未检测到标准 CSV，可优先查看目录中的索引或说明文件。
(1) analyze negative reviews and feedback from guests of fast food restaurant;
(2) get an idea about how various fast food restaurants do in different cities (what are the different defects in services, food, etc);
(3) develop NLP classification models that can classify negative reviews for restaurant managers, help the manager save time on absorbing information, help restaurants improve the business.
(4) try various ML technics, deep learning, xgboost to build NLP models, evaluate models.