In this paper, we propose a generative model to automatically discover the hidden associations between topics words and opinion words. By applying those discovered hidden associations, we construct the opinion scoring models to extract statements which best express opinionists’ standpoints on certain topics. For experiments, we apply our model to the political area. First, we visualize the similarities and dissimilarities between Republican and Democratic senators with respect to various topics. Second, we compare the performance of the opinion scoring models with 14 kinds of methods to find the best ones. We find that sentences extracted by our opinion scoring models can effectively express opinionists’ standpoints.

This paper presents the Topic-Aspect Model (TAM), a Bayesian mixture model which jointly discovers topics and aspects. We broadly define an aspect of a document as a characteristic that spans the document, such as an underlying theme or perspective. Unlike previous models which cluster words by topic or aspect, our model can generate token assignments in both of these dimensions, rather than assuming words come from only one of two orthogonal models. We present two applications of the model. First, we model a corpus of computational linguistics abstracts, and find that the scientific topics identified in the data tend to include both a computational aspect and a linguistic aspect. For example, the computational aspect of GRAMMAR emphasizes parsing, whereas the linguistic aspect focuses on formal languages. Secondly, we show that the model can capture different viewpoints on a variety of topics in a corpus of editorials about the Israeli-Palestinian conflict. We show both qualitative and quantitative improvements in TAM over two other state-of-the-art topic models.

The latent topic model plays an important role in the unsupervised learning from a corpus, which provides a probabilistic interpretation of the corpus in terms of the latent topic space. An underpinning assumption which most of the topic models are based on is that the documents are assumed to be independent of each other. However, this assumption does not hold true in reality and the relations among the documents are available in different ways, such as the citation relations among the research papers. To address this limitation, in this paper we present a Bernoulli Process Topic (BPT) model, where the interdependence among the documents is modeled by a random Bernoulli process. In the BPT model a document is modeled as a distribution over topics that is a mixture of the distributions associated with the related documents. Although BPT aims at obtaining a better document modeling by incorporating the relations among the documents, it could also be applied to many applications including detecting the topics from corpora and clustering the documents. We apply the BPT model to several document collections and the experimental comparisons against several state-of-the-art approaches demonstrate the promising performance.

The social and collaborative nature of dialogue challenges A spoken dialogue system (SDS) has a social role: it supposedly an SDS in many ways. The spontaneity of dialogue gives allows people to communicate with a computer in rise to disfluencies, where a person repeats or interrupts ordinary language. A robust SDS should support coherent herself, produces filled pauses or false starts and selfrepairs. Disfluencies play a fundamental role in dialogue, and habitable dialogue, even when it confronts situations as signals for turn-taking (Gravano, 2009; Sacks, Schegloff for which it has no explicit pre-specified behavior. To ensure robust task completion, however, SDS designers typically and Jefferson, 1974) and for grounding to establish shared produce systems that make a sequence of rigid demands beliefs about the current state of mutual understanding on the user, and thereby lose any semblance of natural (Clark and Schaefer, 1989). Most SDSs handle the content dialogue. The thesis of our work is that a dialogue of the user's utterances, but do not fully integrate the way they address utterance meaning, disfluencies, turn-taking should evolve as a set of agreements that arise from joint and the collaborative nature of grounding.

As microblogging grows in popularity, services like Twitter are coming to support information gathering needs above and beyond their traditional roles as social networks. But most users’ interaction with Twitter is still primarily focused on their social graphs, forcing the often inappropriate conflation of “people I follow” with “stuff I want to read.” We characterize some information needs that the current Twitter interface fails to support, and argue for better representations of content for solving these challenges. We present a scalable implementation of a partially supervised learning model (Labeled LDA) that maps the content of the Twitter feed into dimensions. These dimensions correspond roughly to substance, style, status, and social characteristics of posts. We characterize users and tweets using this model, and present results on two information consumption oriented tasks.

We connect measures of public opinion measured from polls with sentiment measured from text. We analyze several surveys on consumer confidence and political opinion over the 2008 to 2009 period, and find they correlate to sentiment word frequencies in contempora- neous Twitter messages. While our results vary across datasets, in several cases the correlations are as high as 80%, and capture important large-scale trends. The re- sults highlight the potential of text streams as a substi- tute and supplement for traditional polling. consumer confidence and political opinion, and can also pre- dict future movements in the polls. We find that temporal smoothing is a critically important issue to support a suc- cessful model.

Sarcasm is a sophisticated form of speech act widely used in online communities. Automatic recognition of sarcasm is, however, a novel task. Sarcasm recognition could contribute to the performance of review summarization and ranking systems. This paper presents SASI, a novel Semi-supervised Algorithm for Sarcasm Identification that recognizes sarcastic sentences in product reviews. SASI has two stages: semi-supervised pattern acquisition, and sarcasm classification. We experimented on a data set of about 66000 Amazon reviews for various books and products. Using a gold standard in which each sentence was tagged by 3 annotators, we obtained precision of 77% and recall of 83.1% for identifying sarcastic sentences. We found some strong features that characterize sarcastic utterances. However, a combination of more subtle pattern-based features proved more promising in identifying the various facets of sarcasm. We also speculate on the motivation for using sarcasm in online communities and social networks.

Textual sentiment classifiers classify texts into a fixed number of affective classes, such as positive, negative or neutral sentiment, or subjective versus objective information. It has been observed that sentiment classifiers suffer from a lack of generalization capability: a classifier trained on a certain domain generally performs worse on data from another domain. This phenomenon has been attributed to domain-specific affective vocabulary. In this paper, we propose a voting-based thresholding approach, which calibrates a number of existing single-domain classifiers with respect to sentiment data from a new domain. The approach presupposes only a small amount of annotated data from the new domain. We evaluate three criteria for estimating thresholds, and discuss the ramifications of these criteria for the trade-off between classifier performance and manual annotation effort.

This paper addresses the problem of automatically generating domain-specific sentiment clues. The main idea is to bootstrap from a small seed set and generate new clues by using dependencies and collocation information between sentiment clues and sentence-level topics that would be a primary subject of sentiment expression (e.g., event, company, and person). The experiments show that the aggregated clues are effective for sentiment classification.

The American Football betting market provides a particularly attractive domain to study the nexus between public sentiment and the wisdom of crowds. In this paper, we present the first substantial study of the relationship between the NFL betting line and public opinion expressed in blogs and microblogs (Twitter). We perform a large-scale study of four distinct text streams: LiveJournal blogs, RSS blog feeds captured by Spinn3r, Twitter, and traditional news media. Our results show interesting disparities between the first and second halves of each season. We present evidence showing usefulness of sentiment on NFL betting. We demonstrate that a strategy betting roughly 30 games per year identified winner roughly 60% of the time from 2006 to 2009, well beyond what is needed to overcome the bookie's typical commission(53%).