Keyword Extraction for Social Media Short Text

With the booming development of social media in recent years, researchers have begun to pay more attention to extracting personal profiles from information. Keyword extraction plays an important role in extracting personal profiles. However, most of the previous studies are only valid for ordinary text, but not ideal for social media short text. In this paper, we propose an improved method for keyword extraction based on Word2vec and Textrank to solve the unique problem of social media short text. Our approach uses the Word2vec to capture the semantic features between words in selected text, and meanwhile naturally fuses the word frequency, semantic relation and directional relation into Textrank to extract keywords. We conduct the experiments on the three datasets. The experimental results show the superior performance of our method in keyword extraction.