Exploratory topic modeling with distributional semantics
This is an incremental improvement for researchers and practitioners in text analysis, offering better interpretability in exploratory topic modeling.
The paper tackles the problem of limited interpretability and detail in probabilistic topic modeling by proposing an alternative approach that maps topics as a network using distributional semantics, resulting in global topic structures emerging from semantic similarity relations.
As we continue to collect and store textual data in a multitude of domains, we are regularly confronted with material whose largely unknown thematic structure we want to uncover. With unsupervised, exploratory analysis, no prior knowledge about the content is required and highly open-ended tasks can be supported. In the past few years, probabilistic topic modeling has emerged as a popular approach to this problem. Nevertheless, the representation of the latent topics as aggregations of semi-coherent terms limits their interpretability and level of detail. This paper presents an alternative approach to topic modeling that maps topics as a network for exploration, based on distributional semantics using learned word vectors. From the granular level of terms and their semantic similarity relations global topic structures emerge as clustered regions and gradients of concepts. Moreover, the paper discusses the visual interactive representation of the topic map, which plays an important role in supporting its exploration.