Deep Memory Networks for Attitude Identification

We consider the task of identifying attitudes towards a given set of entities from text. Conventionally, this task is decomposed into two separate subtasks: target detection that identifies whether each entity is mentioned in the text, either explicitly or implicitly, and polarity classification that classifies the exact sentiment towards an identified entity (the target) into positive, negative, or neutral. Instead, we show that attitude identification can be solved with an end-to-end machine learning architecture, in which the two subtasks are interleaved by a deep memory network. In this way, signals produced in target detection provide clues for polarity classification, and reversely, the predicted polarity provides feedback to the identification of targets. Moreover, the treatments for the set of targets also influence each other -- the learned representations may share the same semantics for some targets but vary for others. The proposed deep memory network, the AttNet, outperforms methods that do not consider the interactions between the subtasks or those among the targets, including conventional machine learning methods and the state-of-the-art deep learning models.Comment: Accepted to WSDM'1

"Different actors, different tools? Approaching EU and US democracy promotion in the Mediterranean and the Newly Independent States"

This paper contributes to the research agenda on external democracy promotion by attempting a systematic comparison between the democracy promotion endeavors of two major international actors, the European Union (EU) and the United States of America (US). It first outlines an analytical framework that is then tested for its heuristical value, applying it to EU and US democracy promotion efforts on a global and a regional scale, thus comparing different actors as well as across regions. It concludes by highlighting the differences in design and flexibility of their approaches and relates them to a specificity of EU external relations. While both actors can draw on seemingly similar tool boxes for democracy promotion, the EU tends to limit its own scope of action to a rather cooperative approach due to the emphasis it puts on the standardization and (reciprocal) formalization of relations with third countries, including provisions for democracy promotion

Europe in the shadow of financial crisis: Policy Making via Stance Classification

Since 2009, the European Union (EU) is phasing a multi–year financial crisis affecting the stability of its involved countries. Our goal is to gain useful insights on the societal impact of such a strong political issue through the exploitation of topic modeling and stance classification techniques. \ \ To perform this, we unravel public’s stance towards this event and empower citizens’ participation in the decision making process, taking policy’s life cycle as a baseline. The paper introduces and evaluates a bilingual stance classification architecture, enabling a deeper understanding of how citizens’ sentiment polarity changes based on the critical political decisions taken among European countries. \ \ Through three novel empirical studies, we aim to explore and answer whether stance classification can be used to: i) determine citizens’ sentiment polarity for a series of political events by observing the diversity of opinion among European citizens, ii) predict political decisions outcome made by citizens such as a referendum call, ii) examine whether citizens’ sentiments agree with governmental decisions during each stage of a policy life cycle.

Semantic Sentiment Analysis of Twitter Data

Internet and the proliferation of smart mobile devices have changed the way information is created, shared, and spreads, e.g., microblogs such as Twitter, weblogs such as LiveJournal, social networks such as Facebook, and instant messengers such as Skype and WhatsApp are now commonly used to share thoughts and opinions about anything in the surrounding world. This has resulted in the proliferation of social media content, thus creating new opportunities to study public opinion at a scale that was never possible before. Naturally, this abundance of data has quickly attracted business and research interest from various fields including marketing, political science, and social studies, among many others, which are interested in questions like these: Do people like the new Apple Watch? Do Americans support ObamaCare? How do Scottish feel about the Brexit? Answering these questions requires studying the sentiment of opinions people express in social media, which has given rise to the fast growth of the field of sentiment analysis in social media, with Twitter being especially popular for research due to its scale, representativeness, variety of topics discussed, as well as ease of public access to its messages. Here we present an overview of work on sentiment analysis on Twitter.Comment: Microblog sentiment analysis; Twitter opinion mining; In the Encyclopedia on Social Network Analysis and Mining (ESNAM), Second edition. 201

Hypothesis Only Baselines in Natural Language Inference

We propose a hypothesis only baseline for diagnosing Natural Language Inference (NLI). Especially when an NLI dataset assumes inference is occurring based purely on the relationship between a context and a hypothesis, it follows that assessing entailment relations while ignoring the provided context is a degenerate solution. Yet, through experiments on ten distinct NLI datasets, we find that this approach, which we refer to as a hypothesis-only model, is able to significantly outperform a majority class baseline across a number of NLI datasets. Our analysis suggests that statistical irregularities may allow a model to perform NLI in some datasets beyond what should be achievable without access to the context.Comment: Accepted at *SEM 2018 as long paper. 12 page