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39
Get out the vote: Determining support or opposition from Congressional floor-debate transcripts
- In Proceedings of EMNLP
, 2006
"... We investigate whether one can determine from the transcripts of U.S. Congressional floor debates whether the speeches represent support of or opposition to proposed legislation. To address this problem, we exploit the fact that these speeches occur as part of a discussion; this allows us to use sou ..."
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Cited by 149 (4 self)
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We investigate whether one can determine from the transcripts of U.S. Congressional floor debates whether the speeches represent support of or opposition to proposed legislation. To address this problem, we exploit the fact that these speeches occur as part of a discussion; this allows us to use sources of information regarding relationships between discourse segments, such as whether a given utterance indicates agreement with the opinion expressed by another. We find that the incorporation of such information yields substantial improvements over classifying speeches in isolation. 1
Recognizing stances in ideological on-line debates
- In CAAGET’10, NAACL-HLT’10
, 2010
"... This work explores the utility of sentiment and arguing opinions for classifying stances in ideological debates. In order to capture arguing opinions in ideological stance taking, we construct an arguing lexicon automatically from a manually annotated corpus. We build supervised systems employing se ..."
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Cited by 38 (2 self)
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This work explores the utility of sentiment and arguing opinions for classifying stances in ideological debates. In order to capture arguing opinions in ideological stance taking, we construct an arguing lexicon automatically from a manually annotated corpus. We build supervised systems employing sentiment and arguing opinions and their targets as features. Our systems perform substantially better than a distribution-based baseline. Additionally, by employing both types of opinion features, we are able to perform better than a unigrambased system. 1
Dissimilarity in graph-based semisupervised classification
- Eleventh International Conference on Artificial Intelligence and Statistics (AISTATS
, 2007
"... Label dissimilarity specifies that a pair of examples probably have different class labels. We present a semi-supervised classification algorithm that learns from dissimilarity and similarity information on labeled and unlabeled data. Our approach uses a novel graphbased encoding of dissimilarity th ..."
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Cited by 38 (2 self)
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Label dissimilarity specifies that a pair of examples probably have different class labels. We present a semi-supervised classification algorithm that learns from dissimilarity and similarity information on labeled and unlabeled data. Our approach uses a novel graphbased encoding of dissimilarity that results in a convex problem, and can handle both binary and multiclass classification. Experiments on several tasks are promising. 1
Taking sides: User classification for informal online political discourse
- Internet Research
, 2008
"... To evaluate and extend existing natural language processing techniques into the domain of informal online political discussions. Design/methodology/approach A database of postings from a U.S. political discussion site was collected, along with self-reported political orientation data for the users. ..."
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Cited by 25 (0 self)
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To evaluate and extend existing natural language processing techniques into the domain of informal online political discussions. Design/methodology/approach A database of postings from a U.S. political discussion site was collected, along with self-reported political orientation data for the users. A variety of sentiment analysis, text classification, and social network analysis methods were applied to the postings and evaluated against the users ’ self-descriptions. Findings Purely text-based methods performed poorly, but could be improved using techniques which took into account the users ’ position in the online community. Research limitations The techniques we applied here are fairly simple, and more sophisticated learning algorithms may yield better results for text-based classification. Practical implications This work suggests that social network analysis is an important tool for
Predicting Response to Political Blog Posts with Topic Models
"... In this paper we model discussions in online political weblogs (blogs). To do this, we extend Latent Dirichlet Allocation, introduced by Blei et al. (2003), in various ways to capture different characteristics of the data. Our models jointly describe the generation of the primary documents (“posts”) ..."
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Cited by 23 (5 self)
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In this paper we model discussions in online political weblogs (blogs). To do this, we extend Latent Dirichlet Allocation, introduced by Blei et al. (2003), in various ways to capture different characteristics of the data. Our models jointly describe the generation of the primary documents (“posts”) as well as the authorship and, optionally, the contents of the blog community’s verbal reactions to each post (“comments”). We evaluate our model on a novel “comment prediction ” task where the models are used to predict comment activity on a given post. We also provide a qualitative discussion about what the models discover. 1
A Machine Learning Approach to Sentiment Analysis in Multilingual Web Texts
- Information Retrieval
, 2009
"... Sentiment analysis, also called opinion mining, is a form of information extraction from text of growing research and commercial interest. In this paper we present our machine learning experiments with regard to sentiment analysis in blog, review and forum texts found on the World Wide Web and writt ..."
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Cited by 12 (1 self)
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Sentiment analysis, also called opinion mining, is a form of information extraction from text of growing research and commercial interest. In this paper we present our machine learning experiments with regard to sentiment analysis in blog, review and forum texts found on the World Wide Web and written in English, Dutch and French. We train from a set of example sentences or statements that are manually annotated as positive, negative or neutral with regard to a certain entity. We are interested in the feelings that people express with regard to certain consumption products. We learn and evaluate several classification models that can be configured in a cascaded pipeline. We have to deal with several problems, being the noisy character of the input texts, the attribution of the sentiment to a particular entity and the small size of the training set. We succeed to identify positive, negative and neutral feelings to the entity under consideration with ca. 83 % accuracy for English texts based on unigram features augmented with linguistic features. The accuracy results of processing the Dutch and French texts are ca. 70 % and 68 % respectively due to the larger variety of the linguistic expressions that more often diverge from standard language, thus demanding more training patterns. In addition, our experiments give us insights into the portability of the learned models across domains and languages. A substantial part of the article investigates the role of active learning techniques for reducing the number of examples to be manually annotated. Keywords Opinion mining – information tracking – cross-language learning – active learning 1
Measuring ideological proportions in political speeches
- In Proceedings of the Conference on Empirical Methods in Natural Language Processing and Natural Language Learning
, 2013
"... We seek to measure political candidates ’ ideological positioning from their speeches. To accomplish this, we infer ideological cues from a corpus of political writings annotated with known ideologies. We then represent the speeches of U.S. Presidential candidates as sequences of cues and lags (fill ..."
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Cited by 12 (3 self)
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We seek to measure political candidates ’ ideological positioning from their speeches. To accomplish this, we infer ideological cues from a corpus of political writings annotated with known ideologies. We then represent the speeches of U.S. Presidential candidates as sequences of cues and lags (filler distinguished only by its length in words). We apply a domain-informed Bayesian HMM to infer the proportions of ideologies each candidate uses in each campaign. The results are validated against a set of preregistered, domain expertauthored hypotheses. 1
Graph-based user classification for informal online political discourse
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Personalized Recommendation of User Comments via Factor Models
"... In recent years, the amount of user-generated opinionated texts (e.g., reviews, user comments) continues to grow at a rapid speed: featured news stories on a major event easily attract thousands of user comments on a popular online News service. How to consume subjective information of this volume b ..."
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Cited by 9 (0 self)
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In recent years, the amount of user-generated opinionated texts (e.g., reviews, user comments) continues to grow at a rapid speed: featured news stories on a major event easily attract thousands of user comments on a popular online News service. How to consume subjective information of this volume becomes an interesting and important research question. In contrast to previous work on review analysis that tried to filter or summarize information for a generic average user, we explore a different direction of enabling personalized recommendation of such information. For each user, our task is to rank the comments associated with a given article according to personalized user preference (i.e., whether the user is likely to like or dislike the comment). To this end, we propose a factor model that incorporates rater-comment and rater-author interactions simultaneously in a principled way. Our full model significantly outperforms strong baselines as well as related models that have been considered in previous work. 1
Political leaning categorization by exploring subjectivities in political blogs
- In In Proceedings, 4th International Conference on Data Mining
, 2008
"... Abstract — This paper addresses a relatively new text categorization problem: classifying a political blog as either ‘liberal ’ or ‘conservative’, based on its political leaning. Instead of simply using “Bag of Words ” features (BoW) as in previous work, we have explored subjectivity manifested in b ..."
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Cited by 7 (0 self)
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Abstract — This paper addresses a relatively new text categorization problem: classifying a political blog as either ‘liberal ’ or ‘conservative’, based on its political leaning. Instead of simply using “Bag of Words ” features (BoW) as in previous work, we have explored subjectivity manifested in blogs and used subjectivity information thus found to help build political leaning classifiers. Specifically, our subjectivity based approach is two fold: 1) we identify subjective sentences that contain at least two strong subjective clues based on the General Inquirer dictionary; 2) from subjective sentences identified, we extract opinion expressions and BoW features to build political leaning classifiers. Experiments with a political blog corpus we built show that by using features from subjective sentences can significantly improve the classification performance. In addition, by extracting opinion expressions from subjective sentences, we are able to reveal opinions that are characteristic of a specific political orientation to some extent.