Tracking Dengue Epidemics using Twitter Content Classification and Topic Modelling

Detecting and preventing outbreaks of mosquito-borne diseases such as Dengue and Zika in Brasil and other tropical regions has long been a priority for governments in affected areas. Streaming social media content, such as Twitter, is increasingly being used for health vigilance applications such as flu detection. However, previous work has not addressed the complexity of drastic seasonal changes on Twitter content across multiple epidemic outbreaks. In order to address this gap, this paper contrasts two complementary approaches to detecting Twitter content that is relevant for Dengue outbreak detection, namely supervised classification and unsupervised clustering using topic modelling. Each approach has benefits and shortcomings. Our classifier achieves a prediction accuracy of about 80\% based on a small training set of about 1,000 instances, but the need for manual annotation makes it hard to track seasonal changes in the nature of the epidemics, such as the emergence of new types of virus in certain geographical locations. In contrast, LDA-based topic modelling scales well, generating cohesive and well-separated clusters from larger samples. While clusters can be easily re-generated following changes in epidemics, however, this approach makes it hard to clearly segregate relevant tweets into well-defined clusters.Comment: Procs. SoWeMine - co-located with ICWE 2016. 2016, Lugano, Switzerlan

Multi-task Learning of Pairwise Sequence Classification Tasks Over Disparate Label Spaces

We combine multi-task learning and semi-supervised learning by inducing a joint embedding space between disparate label spaces and learning transfer functions between label embeddings, enabling us to jointly leverage unlabelled data and auxiliary, annotated datasets. We evaluate our approach on a variety of sequence classification tasks with disparate label spaces. We outperform strong single and multi-task baselines and achieve a new state-of-the-art for topic-based sentiment analysis.Comment: To appear at NAACL 2018 (long

Sarcasm Detection in English and Arabic Tweets Using Transformer Models

This thesis describes our approach toward the detection of sarcasm and its various types in English and Arabic Tweets through methods in deep learning. There are five problems we attempted: (1) detection of sarcasm in English Tweets, (2) detection of sarcasm in Arabic Tweets, (3) determining the type of sarcastic speech subcategory for English Tweets, (4) determining which of two semantically equivalent English Tweets is sarcastic, and (5) determining which of two semantically equivalent Arabic Tweets is sarcastic. All tasks were framed as classification problems, and our contributions are threefold: (a) we developed an English binary classifier system with RoBERTa, (b) an Arabic binary classifier with XLM-RoBERTa, and (c) an English multilabel classifier with BERT. Pre-processing steps are taken with labeled input data prior to tokenization, such as extracting and appending verbs/adjectives or representative/significant keywords to the end of an input tweet to help the models better understand and generalize sarcasm detection. We also discuss the results of simple data augmentation techniques to improve the quality of the given training dataset as well as an alternative approach to the question of multilabel sequence classification. Ultimately, our systems place us in the top 14 participants for each of the five tasks in a sarcasm detection competition

Text Analysis of Airline Tweets

By acting as a succinct summary, keywords and key phrases can be a useful tool for swiftly assessing enormous amounts of textual material. A keyword is defined as a word that briefly and accurately characterises the subject, or an aspect of the subject, presented in a text, according to the International Encyclopaedia of Information and Library Science (Bolger et al., 1989) (Feather et al., 1996). People are more likely to complain when they are anxious, according to research (Bolger et al., 1989)(Meier et al., 2013), and moods are affected by time (Ryan et al., 2010). Due to this study, airlines will have a tool to calibrate and judge the positivity/negativity of tweets based on the day of the week, which is a topic that has yet to be researched. We want to do text and sentiment analysis on extracted airline travel tweets, taking into account when the tweet was ‘tweeted’ and if it had a good or negative impact

Building a Sentiment Corpus of Tweets in Brazilian Portuguese

The large amount of data available in social media, forums and websites motivates researches in several areas of Natural Language Processing, such as sentiment analysis. The popularity of the area due to its subjective and semantic characteristics motivates research on novel methods and approaches for classification. Hence, there is a high demand for datasets on different domains and different languages. This paper introduces TweetSentBR, a sentiment corpora for Brazilian Portuguese manually annotated with 15.000 sentences on TV show domain. The sentences were labeled in three classes (positive, neutral and negative) by seven annotators, following literature guidelines for ensuring reliability on the annotation. We also ran baseline experiments on polarity classification using three machine learning methods, reaching 80.99% on F-Measure and 82.06% on accuracy in binary classification, and 59.85% F-Measure and 64.62% on accuracy on three point classification.Comment: Accepted for publication in 11th International Conference on Language Resources and Evaluation (LREC 2018

Sarcasm Detection in a Disaster Context

During natural disasters, people often use social media platforms such as Twitter to ask for help, to provide information about the disaster situation, or to express contempt about the unfolding event or public policies and guidelines. This contempt is in some cases expressed as sarcasm or irony. Understanding this form of speech in a disaster-centric context is essential to improving natural language understanding of disaster-related tweets. In this paper, we introduce HurricaneSARC, a dataset of 15,000 tweets annotated for intended sarcasm, and provide a comprehensive investigation of sarcasm detection using pre-trained language models. Our best model is able to obtain as much as 0.70 F1 on our dataset. We also demonstrate that the performance on HurricaneSARC can be improved by leveraging intermediate task transfer learning. We release our data and code at https://github.com/tsosea2/HurricaneSarc