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皇家马德里 vs 利物浦期间的推文

皇家马德里 vs 利物浦期间的推文

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Sports,Football,Data Visualization,Text Mining Classification

数据结构 ? 1856.52M

    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

    Context Data set containing Tweets captured during the **2018 UEFA Champions League Final** between **Real Madrid** and **Liverpool**. Content All Twitter APIs that return Tweets provide that data encoded using JavaScript Object Notation (JSON). **JSON** is based on key-value pairs, with named attributes and associated values. The JSON file include the following objects and attributes: * **[Tweet](https://developer.twitter.com/en/docs/tweets/data-dictionary/overview/tweet-object)** - Tweets are the basic atomic building block of all things Twitter. The Tweet object has a long list of ‘root-level’ attributes, including fundamental attributes such as `id`, `created_at`, and `text`. Tweet child objects include `user`, `entities`, and `extended_entities.` Tweets that are geo-tagged will have a `place` child object. + **[User](https://developer.twitter.com/en/docs/tweets/data-dictionary/overview/user-object)** - Contains public Twitter account metadata and describes the author of the Tweet with attributes as `name`, `description`, `followers_count`, `friends_count`, etc. + **[Entities](https://developer.twitter.com/en/docs/tweets/data-dictionary/overview/entities-object)** - Provide metadata and additional contextual information about content posted on Twitter. The `entities` section provides arrays of common things included in Tweets: hashtags, user mentions, links, stock tickers (symbols), Twitter polls, and attached media. + **[Extended Entities](https://developer.twitter.com/en/docs/tweets/data-dictionary/overview/extended-entities-object)** - All Tweets with attached photos, videos and animated GIFs will include an `extended_entities` JSON object. + **[Places](https://developer.twitter.com/en/docs/tweets/data-dictionary/overview/geo-objects)** - Tweets can be associated with a location, generating a Tweet that has been ‘geo-tagged.’ More information [here](https://developer.twitter.com/en/docs/tweets/data-dictionary/overview/intro-to-tweet-json). Acknowledgements I used the `filterStream()` function to open a connection to Twitter's Streaming API, using the keyword **#UCLFinal**. The capture started on **Saturday, May 27th 6:45 pm UCT** (beginning of the match) and finished on **Saturday, May 27th 8:45 pm UCT**. Inspiration - Time analysis - Try text mining! - Cross-language differences in Twitter - Use this data to produce a sentiment analysis - Twitter geolocation - Network analysis: graph theory, metrics and properties of the network, community detection, network visualization, etc.
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