Social media users generate voluminous data, which can contain meaningful and useful information. One such example is information about locations, which may be useful in applications such as marketing and security monitoring. There are two types of locations: location entities mentioned in the text of the messages and the physical locations of users. Extracting the first type of locations is not trivial because the location entities in the text are often ambiguous. In this thesis, we implement a sequential classification model with conditional random fields and a rule-based disambiguation model, which are applied to Twitter messages (tweets) and are shown to handle the ambiguous location entities in our dataset reasonably well.

Only very few user disclose their physical locations; in order to automatically detect their locations, many approaches have been proposed using various types of information, including the tweets posted by the users. It is not easy to infer the original locations from text data, because text tends to be noisy, particularly in social media. Recently, deep learning techniques have been shown to reduce the error rate of many machine learning tasks, due to their ability to learn meaningful representations of input data. We investigate the potential of building a deep-learning architecture to infer the location of Twitter users based merely on their tweets. We found that stacked denoising auto-encoders are well suited for this task, with results comparable to state-of-the-art models.

Finally, we combine the two models above with a third-party sentiment analysis tool and obtain an intelligent social media monitoring system. We show a demo of the system and that it is able to predict and visualize the locations and sentiments contained in a stream of tweets related to mobile phone brands, a typical real world e-business application.