Discovering polarity change in word meanings

Explaining the science

In natural language processing, sentiment analysis (SA) identifies a text's polarity. Existing approaches to SA do not account for a time factor. This is an important limitation, as the polarity of words can often change quickly. For example, the word ‘sick’ has recently acquired the meaning of ‘cool’ alongside the previous one of ‘unwell’. Being able to identify and react to this change is crucial for keeping SA systems up to date.

Existing SA research usually requires large amounts of labelled data. Crucially, by transferring domain knowledge from ongoing interaction with Thompson Reuters, this project is using financial data to efficiently produce sentiment-labelled data.

In algorithmic research, this project also seeks to further state of the art in spectral clustering methods – grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense) to each other than to those in other groups (clusters). The project will be extending constrained clustering to an online setting, where constraints arrive incrementally.

The main challenge of incremental clustering is how to efficiently update a clustering so that it satisfies both the new and old constraints, as opposed to re-clustering the entire data set from scratch. A related problem involves incorporating user feedback in the clustering loop, a task common in active learning where the algorithm chooses constraints and queries the user.