Natural language processing techniques are increasingly applied to identify social trends and predict behavior based on large text collections. Existing methods typically rely on surface lexical and syntactic information. Yet, research in psychology shows that patterns of human conceptualisation, such as metaphorical framing, are reliable predictors of human expectations and decisions. In this paper, we present a method to learn patterns of metaphorical framing from large text collections, using statistical techniques. We apply the method to data in three different languages and evaluate the identified patterns, demonstrating their psychological validity.
NLP techniques have been successfully used for a number of tasks in political science, including automatically estimating the influence of particular politicians in the US senate @cite_56 , identifying lexical features that differentiate political rhetoric of opposing parties @cite_10 , predicting voting patterns of politicians based on their use of language @cite_13 , and predicting political affiliation of Twitter users @cite_44 . Other approaches @cite_39 @cite_17 @cite_0 @cite_16 @cite_57 detected the contrasting perspectives on a set of topics attested in distinct corpora using LDA topic modelling. Some works focused on subjectivity detection, identifying opinion, evaluation, and speculation in text @cite_22 . There is a large body of work on identifying such opinions and attributing it to specific people @cite_27 @cite_4 . While successful in their tasks, these methods rely on surface linguistic cues, rather than generalising patterns of human association and conceptualisation, which limits the information they discover to that explicitly stated.
@cite_4 @cite_22 @cite_10 @cite_39 @cite_44 @cite_56 @cite_0 @cite_57 @cite_27 @cite_16 @cite_13 @cite_17
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The rapid and continuous growth of social networking sites has led to the emergence of many communities of communicating groups. Many of these groups discuss ideological and political topics. It is not uncommon that the participants in such discussions split into two or more subgroups. The members of each subgroup share the same opinion toward the discussion topic and are more likely to agree with members of the same subgroup and disagree with members from opposing subgroups. In this paper, we propose an unsupervised approach for automatically detecting discussant subgroups in online communities. We analyze the text exchanged between the participants of a discussion to identify the attitude they carry toward each other and towards the various aspects of the discussion topic. We use attitude predictions to construct an attitude vector for each discussant. We use clustering techniques to cluster these vectors and, hence, determine the subgroup membership of each participant. We compare our methods to text clustering and other baselines, and show that our method achieves promising results. We introduce a technique for identifying the most salient participants in a discussion. Our method, MavenRank is based on lexical centrality: a random walk is performed on a graph in which each node is a participant in the discussion and an edge links two participants who use similar rhetoric. As a test, we used MavenRank to identify the most influential members of the US Senate using data from the US Congressional Record and used committee ranking to evaluate the output. Our results show that MavenRank scores are largely driven by committee status in most topics, but can capture speaker centrality in topics where speeches are used to indicate ideological position instead of influence legislation. This paper presents a novel opinion mining research problem, which is called Contrastive Opinion Modeling (COM). Given any query topic and a set of text collections from multiple perspectives, the task of COM is to present the opinions of the individual perspectives on the topic, and furthermore to quantify their difference. This general problem subsumes many interesting applications, including opinion summarization and forecasting, government intelligence and cross-cultural studies. We propose a novel unsupervised topic model for contrastive opinion modeling. It simulates the generative process of how opinion words occur in the documents of different collections. The ad hoc opinion search process can be efficiently accomplished based on the learned parameters in the model. The difference of perspectives can be quantified in a principled way by the Jensen-Shannon divergence among the individual topic-opinion distributions. An extensive set of experiments have been conducted to evaluate the proposed model on two datasets in the political domain: 1) statement records of U.S. senators; 2) world news reports from three representative media in U.S., China and India, respectively. The experimental results with both qualitative and quantitative analysis have shown the effectiveness of the proposed model. We explore Debatepedia, a communityauthored encyclopedia of sociopolitical debates, as evidence for inferring a lowdimensional, human-interpretable representation in the domain of issues and positions. We introduce a generative model positing latent topics and cross-cutting positions that gives special treatment to person mentions and opinion words. We evaluate the resulting representation’s usefulness in attaching opinionated documents to arguments and its consistency with human judgments about positions. The wikileaks documents or the economic crises in Ireland and Portugal are some of the controversial topics being played on the news everyday. Each of these topics has many different aspects, and there is no absolute, simple truth in answering questions such as: should the EU guarantee the financial stability of each member country, or should the countries themselves be solely responsible? To understand the landscape of opinions, it would be helpful to know which politician or other stakeholder takes which position - support or opposition - on these aspects of controversial topics. In this paper, we describe our system, named OpinioNetIt (pronounced similar to "opinionated"), which aims to automatically derive a map of the opinions-people network from news and other Web documents. We build this network as follows. First, we make use of a small number of generic seeds to identify controversial phrases from text. These phrases are then clustered and organized into a hierarchy of topics. Second, opinion holders are identified for each topic and their opinions (either supporting or opposing the topic) are extracted. Third, the known topics and people are used to construct a lexicon phrases indicating support or opposition. Finally, the lexicon is uses to identify more opinion holders, opinions and topics. Our system currently consists of approximately 30000 person-opinion-topic triples. Our evaluation shows that OpinioNetIt has high accuracy. We develop several predictive models linking legislative sentiment to legislative text. Our models, which draw on ideas from ideal point estimation and topic models, predict voting patterns based on the contents of bills and infer the political leanings of legislators. With supervised topics, we provide an exploratory window into how the language of the law is correlated with political support. We also derive approximate posterior inference algorithms based on variational methods. Across 12 years of legislative data, we predict specific voting patterns with high accuracy.