Gaussian Processes for One-class and Binary Classification of Crisis-related Tweets

The Twitter Stream API offers the possibility to develop (near) real-time methods and applications to detect and monitor impacts of crisis events and their changes over time. As demonstrated by various related research, the content of individual tweets or even entire thematic trends can be utilized to support disaster management, fill information gaps and augment results of satellite-based workflows as well as to extend and improve disaster management databases. Considering the sheer volume of incoming tweets, it is necessary to automatically identify the small number of crisis-relevant tweets and present them in a manageable way. Current approaches for identifying crisis-related content focus on the use of supervised models that decide on the relevance of each tweet individually. Although supervised models can efficiently process the high number of incoming tweets, they have to be extensively pre-trained. Furthermore, the models do not capture the history of already processed messages. During a crisis, various and unique sub-events can occur that are likely to be not covered by the respective supervised model and its training data. Unsupervised learning offers both, to take into account tweets from the past, and a higher adaptive capability, which in turn allows a customization to the specific needs of different disasters. From a practical point of view, drawbacks of unsupervised methods are the higher computational costs and the potential need of user interaction for result interpretation. In order to enhance the limited generalization capabilities of pre-trained models as well as to speed up and guide unsupervised learning, we propose a combination of both concepts. A successive clustering of incoming tweets allows to semantically aggregate the stream data, whereas pre-trained models allow to identify potentially crisis-relevant clusters. Besides the identification of potentially crisis-related content based on semantically aggregated clusters, this approach offers a sound foundation for visualizations, and further related tasks, like event detection as well as the extraction of detailed information about the temporal or spatial development of events. Our work focuses on analyzing the entire freely available Twitter stream by combining an interval-based semantic clustering with an supervised machine learning model for identifying crisis-related messages. The stream is divided into intervals, e.g. of one hour, and each tweet is projected into a numerical vector by using state-of-the-art sentence embeddings. The embeddings are then grouped by a parametric Chinese Restaurant Process clustering. At the end of each interval, a pre-trained feed-forward neural network decides whether a cluster contains crisis-related tweets. With a further developed concept of cluster chains and central centroids, crisis-related clusters of different intervals can be linked in a topic- and even subtopic-related manner. Initial results show that the hybrid approach can significantly improve the results of pre-trained supervised methods. This is especially true for categories in which the supervised model could not be sufficiently pre-trained due to missing labels. In addition, the semantic clustering of tweets offers a flexible and customizable procedure, resulting in a practical summary of topic-specific stream content

Multilingual sentiment analysis in social media.

252 p.This thesis addresses the task of analysing sentiment in messages coming from social media. The ultimate goal was to develop a Sentiment Analysis system for Basque. However, because of the socio-linguistic reality of the Basque language a tool providing only analysis for Basque would not be enough for a real world application. Thus, we set out to develop a multilingual system, including Basque, English, French and Spanish.The thesis addresses the following challenges to build such a system:- Analysing methods for creating Sentiment lexicons, suitable for less resourced languages.- Analysis of social media (specifically Twitter): Tweets pose several challenges in order to understand and extract opinions from such messages. Language identification and microtext normalization are addressed.- Research the state of the art in polarity classification, and develop a supervised classifier that is tested against well known social media benchmarks.- Develop a social media monitor capable of analysing sentiment with respect to specific events, products or organizations

Gaussian Processes for Text Regression

Text Regression is the task of modelling and predicting numerical indicators or response variables from textual data. It arises in a range of different problems, from sentiment and emotion analysis to text-based forecasting. Most models in the literature apply simple text representations such as bag-of-words and predict response variables in the form of point estimates. These simplifying assumptions ignore important information coming from the data such as the underlying uncertainty present in the outputs and the linguistic structure in the textual inputs. The former is particularly important when the response variables come from human annotations while the latter can capture linguistic phenomena that go beyond simple lexical properties of a text. In this thesis our aim is to advance the state-of-the-art in Text Regression by improving these two aspects, better uncertainty modelling in the response variables and improved text representations. Our main workhorse to achieve these goals is Gaussian Processes (GPs), a Bayesian kernelised probabilistic framework. GP-based regression models the response variables as well-calibrated probability distributions, providing additional information in predictions which in turn can improve subsequent decision making. They also model the data using kernels, enabling richer representations based on similarity measures between texts. To be able to reach our main goals we propose new kernels for text which aim at capturing richer linguistic information. These kernels are then parameterised and learned from the data using efficient model selection procedures that are enabled by the GP framework. Finally we also capitalise on recent advances in the GP literature to better capture uncertainty in the response variables, such as multi-task learning and models that can incorporate non-Gaussian variables through the use of warping functions. Our proposed architectures are benchmarked in two Text Regression applications: Emotion Analysis and Machine Translation Quality Estimation. Overall we are able to obtain better results compared to baselines while also providing uncertainty estimates for predictions in the form of posterior distributions. Furthermore we show how these models can be probed to obtain insights about the relation between the data and the response variables and also how to apply predictive distributions in subsequent decision making procedures