隨著科技進步，社群媒體變成人們分享生活與獲得感興趣訊息的重要媒介，而通常人們接收到的訊息蘊含各式各樣的議題，這些大量的訊息通常是雜亂無章的，人們很難從中直接獲得有價值的資訊，若要最大程度地挖掘這些資訊的價值，便會尋求機器的協助，此時機器學習即是其中一種常見手段，其被廣泛應用於資料分類任務中。在一般機器學習的開發流程中，需自行標記資料及撰寫程式，往往曠時費力，為求降低標記成本且非專業人士也能夠建立模型以進行分類任務，我們設計一無程式碼開發平台，以社群媒體中的訊息作為資料來源，減少使用者收集與過濾資料的功夫，結合主動學習方法，降低標記資料的成本，並採取自動化的流程，幫助使用者在不需撰寫程式的情況下完成模型訓練，最終讓使用者能夠根據自己的需求快速地進行分類任務。因此在實務上的挑戰即包括如何讓使用者自訂興趣主題並快速篩選出感興趣的社群訊息及如何讓使用者快速調整目標或做不同應用。在平台完成後，我們進行實驗以驗證主動學習方法的效果，並透過不同的分類主題任務以測試平台的實用性。

With the progress of technology, social media has become an important medium for people to share their daily life and obtain information of interest. In general, the information people receive contains a variety of themes, these large amounts of information are typically disorganized, and it is difficult for people to obtain valuable information from them directly. Therefore, when people want to maximize the value of these information, they may seek the assistance of machines. At this time, machine learning (ML) is one of the standard methods, which is widely used in data classification tasks. Generally, the development of a machine learning process requires data labeling and programming, which is often time-consuming and laborious. To help the non-professionals build models with only a few efforts of data labeling for conducting classification tasks, we design a no-code development platform (NCDP) that uses messages which are crawled from social media as the data source to reduce the effort of users to collect and filter data, combines the active learning method to lower the cost of users to create datasets and adopts an automatic process that helps users complete model training without the necessity of writing programs. Finally, users can quickly conduct classification tasks. Therefore, practical challenges include how users can get social messages of their interests according to specific topics and how users can fine-tune their targets and conduct different applications. After the system is completed, we verify the effect of the active learning method through experiments and test the practicability of the platform through different classification topic tasks.

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