On Developing a Social Message Classification Platform for User-Specified Topics

The information people receive from social media contains a variety of topics, and these massive amounts of information are often disorganized. It is difficult for people to get valuable information directly from it. The advantage of our approach is two-fold: To accommodate users of various interests, and Human-in-The-Loop can be responsible for guiding AI system learning. And machine learning is one of the standard methods that is widely used for data classification tasks. The development of machine learning processes often requires efforts of data labeling and programming, which is often time-consuming and labor-intensive. To help non-experts better understand the model training process, we design a no-code data classification platform. It uses messages crawled from social media as data sources, filters data, and incorporates active learning methods to reduce the cost of creating datasets. Our platform uses an automated process to help users complete model training through simple web operations without programming. The ultimate goal is that users can intervene in the process of model training, quickly perform classification tasks and obtain information according to their needs.