Select Language

AI社区

公开数据集

一袋话遇上一袋爆米花

一袋话遇上一袋爆米花

127.64M
209 浏览
0 喜欢
0 次下载
0 条讨论
Arts and Entertainment,Movies and TV Shows,NLP Classification

数据结构 ? 127.64M

    Data Structure ?

    * 以上分析是由系统提取分析形成的结果,具体实际数据为准。

    README.md

    ## Data Set The labelled data set consists of 50,000 IMDB movie reviews, specially selected for sentiment analysis. The sentiment of reviews is binary, meaning the IMDB rating < 5 results in a sentiment score of 0, and rating >=7 have a sentiment score of 1. No individual movie has more than 30 reviews. The 25,000 review labelled training set does not include any of the same movies as the 25,000 review test set. In addition, there are another 50,000 IMDB reviews provided without any rating labels. ## File descriptions - **labeledTrainData -** The labelled training set. The file is tab-delimited and has a header row followed by 25,000 rows containing an id, sentiment, and text for each review. - **testData -** The test set. The tab-delimited file has a header row followed by 25,000 rows containing an id and text for each review. Your task is to predict the sentiment for each one. - **unlabeledTrainData -** An extra training set with no labels. The tab-delimited file has a header row followed by 50,000 rows containing an id and text for each review. - **sampleSubmission -** A comma-delimited sample submission file in the correct format. ## Data fields - **id -** Unique ID of each review - **sentiment -** Sentiment of the review; 1 for positive reviews and 0 for negative reviews - **review -** Text of the review
    ×

    帕依提提提温馨提示

    该数据集正在整理中,为您准备了其他渠道,请您使用

    注:部分数据正在处理中,未能直接提供下载,还请大家理解和支持。
    暂无相关内容。
    暂无相关内容。
    • 分享你的想法
    去分享你的想法~~

    全部内容

      欢迎交流分享
      开始分享您的观点和意见,和大家一起交流分享.
    所需积分:0 去赚积分?
    • 209浏览
    • 0下载
    • 0点赞
    • 收藏
    • 分享