Improving the utility of social media with Natural Language Processing

Social media has been an attractive target for many natural language processing (NLP) tasks and applications in recent years. However, the unprecedented volume of data and the non-standard language register cause problems for off-the-shelf NLP tools. This thesis investigates the broad question of how NLP-based text processing can improve the utility (i.e., the effectiveness and efficiency) of social media data. In particular, text normalisation and geolocation prediction are closely examined in the context of Twitter text processing. Text normalisation is the task of restoring non-standard words to their standard forms. For instance, earthquick and 2morrw should be transformed into “earthquake” and “tomorrow”, respectively. Non-standard words often cause problems for existing tools trained on edited text sources such as newswire text. By applying text normalisation to reduce unknown non-standard words, the accuracy of NLP tools and downstream applications is expected to increase. In this thesis, I explore and develop lexical normalisation methods for Twitter text. I shift the focus of text normalisation from a cascaded token-based approach to a type-based approach using a combined lexicon, based on the analysis of existing and developed text normalisation methods. The type-based method achieved the state-of-the-art end-to-end normalisation accuracy at the time of publication, i.e., 0.847 precision and 0.630 recall on a benchmark dataset. Furthermore, it is simple, lightweight and easily integrable which is particularly well suited to large-scale data processing. Additionally, the effectiveness of the proposed normalisation method is shown in non-English text normalisation and other NLP tasks and applications. Geolocation prediction estimates a user’s primary location based on the text of their posts. It enables location-based data partitioning, which is crucial to a range of tasks and applications such as local event detection. The partitioned location data can improve both the efficiency and the effectiveness of NLP tools and applications. In this thesis, I identify and explore several factors that affect the accuracy of textbased geolocation prediction in a unified framework. In particular, an extensive range of feature selection methods is compared to determine the optimised feature set for the geolocation prediction model. The results suggest feature selection is an effective method for improving the prediction accuracy regardless of geolocation model and location partitioning. Additionally, I examine the influence of other factors includ-