KeywordMap: Attention-based Visual Exploration for Keyword Analysis

With the high growth rate of text data, extracting meaningful information from a large corpus becomes increasingly difficult. Keyword extraction and analysis is a common approach to tackle the problem, but it is non-trivial to identify important words in the text and represent the multifaceted properties of those words effectively. Traditional topic modeling based keyword analysis algorithms require hyper-parameters which are often difficult to tune without enough prior knowledge. In addition, the relationships among the keywords are often difficult to obtain. In this paper, we utilize the attention scores extracted from Transformer-based language models to capture word relationships. We propose a domain-driven attention tuning method, guiding the attention to learn domain-specific word relationships. From the attention, we build a keyword network and propose a novel algorithm, Attention-based Word Influence (AWI), to compute how influential each word is in the network. An interactive visual analytics system, KeywordMap, is developed to support multi-level analysis of keywords and keyword relationships through coordinated views. We measure the quality of keywords captured by our AWI algorithm quantitatively. We also evaluate the usefulness and effectiveness of KeywordMap through case studies.