Within the area of general-purpose finegrained subjectivity analysis, opinion topic identification has, to date, received little attention due to both the difficulty of the task and the lack of appropriately annotated resources. In this paper, we provide an operational definition of opinion topic and present an algorithm for opinion topic identification that, following our new definition, treats the task as a problem in topic coreference resolution. We develop a methodology for the manual annotation of opinion topics and use it to annotate topic information for a portion of an existing general-purpose opinion corpus. In experiments using the corpus, our topic identification approach statistically significantly outperforms several non-trivial baselines according to three evaluation measures. 1

Rapid response to a health epidemic is critical to reduce loss of life. Existing methods mostly rely on expensive surveys of hospitals across the country, typically with lag times of one to two weeks for influenza reporting, and even longer for less common diseases. In response, there have been several recently proposed solutions to estimate a population’s health from Internet activity, most notably Google’s Flu Trends service, which correlates search term frequency with influenza statistics reported by the Centers for Disease Control and Prevention (CDC). In this paper, we analyze messages posted on the micro-blogging site Twitter.com to determine if a similar correlation can be uncovered. We propose several methods to identify influenza-related messages and compare a number of regression models to correlate these messages with CDC statistics. Using over 500,000 messages spanning 10 weeks, we find that our best model achieves a correlation of.78 with CDC statistics by leveraging a document classifier to identify relevant messages.

Fine-grained subjectivity analysis has been the subject of much recent research attention. As a result, the field has gained a number of working definitions, technical approaches and manually annotated corpora that cover many facets of subjectivity. Little work has been done, however, on one aspect of fine-grained opinions – the specification and identification of opinion topics. In particular, due to the difficulty of manual opinion topic annotation, no general-purpose opinion corpus with information about topics of fine-grained opinions currently exists. In this paper, we propose a methodology for the manual annotation of opinion topics and use it to annotate a portion of an existing general-purpose opinion corpus with opinion topic information. Inter-annotator agreement results according to a number of metrics suggest that the annotations are reliable. 1.

User generated content is extremely valuable for mining market intelligence because it is unsolicited. We study the problem of analyzing users ’ sentiment and opinion in their blog, message board, etc. posts with respect to topics expressed as a search query. In the scenario we consider the matches of the search query terms are expanded through coreference and meronymy to produce a set of mentions. The mentions are contextually evaluated for sentiment and their scores are aggregated (using a data structure we introduce call the sentiment propagation graph) to produce an aggregate score for the input entity. An extremely crucial part in the contextual evaluation of individual mentions is finding which sentiment expressions are semantically related to (target) which mentions — this is the focus of our paper. We present an approach where potential target mentions for a sentiment expression are ranked using supervised machine learning (Support Vector Machines) where the main features are the syntactic configurations (typed dependency paths) connecting the sentiment expression and the mention. We have created a large English corpus of product discussions blogs annotated with semantic types of mentions, coreference, meronymy and sentiment targets. The corpus proves that coreference and meronymy are not marginal phenomena but are really central to determining the overall sentiment for the toplevel entity. We evaluate a number of techniques for sentiment targeting and present results which we believe push the current state-of-the-art. 1.

As many popular text genres such as blogs or news contain opinions by multiple sources and about multiple targets, finding the sources and targets of subjective expressions becomes an important sub-task for automatic opinion analysis systems. We argue that while automatic semantic role labeling systems (ASRL) have an important contribution to make, they cannot solve the problem for all cases. Based on the experience of manually annotating opinions, sources, and targets in various genres, we present linguistic phenomena that require knowledge beyond that of ASRL systems. In particular, we address issues relating to the attribution of opinions to sources; sources and targets that are realized as zero-forms; and inferred opinions. We also discuss in some depth that for arguing attitudes we need to be able to recover propositions and not only argued-about entities. A recurrent theme of the discussion is that close attention to specific discourse contexts is needed to identify sources and targets correctly. 1.

The technology behind RSS feeds offers great possibilities to retrieve more news items than ever. In contrast to these technical developments, human capabilities to read all these news items have not increased likewise. To bridge this gap, this paper presents a visual analytics tool for conducting semi-automatic sentiment analysis of large news feeds. While the tool automatically retrieves and analyzes RSS feeds with respect to positive and negative opinion words, the more demanding news analysis of finding trends, spotting peculiarities and putting events into context is left to the human expert. For a solid analysis the news similarity filter enables highlighting of similar or redundant news items. A case study about news related to the US presidential election in 2008 shows how the visual interface of the tool empowers the analyst to draw meaningful conclusions without the effort of reading all news postings. Author Keywords sentiment analysis, opinion mining, information visualization,

Opinion mining has been receiving increasing attention recently, and various approaches have been suggested for mining sentiment information, such as mining attitudes or opinions about a topic or product etc. However, as far as we know, little work has been reported on citation

The web has become an important medium for news delivery and consumption. Fresh content about a variety of topics, events, and places is constantly being created and published on the web by news agencies around the world. As intuitively understood by readers, and studied in journalism, news articles produced by different social groups present different attitudes towards and interpretations of the same news issues. In this paper, we propose a new paradigm for aggregating news articles according to the local news sources associated with the stakeholders of the news issues. This new paradigm provides users the capability to aggregate and browse various local points of view about the news issues in which they are interested. We implement this paradigm in a system called LocalSavvy. LocalSavvy analyzes the news articles provided by users, using knowledge about locations automatically acquired from the web. Based on the analysis of the news issue, the system finds and aggregates local news articles published by official and unofficial news sources associated with the stakeholders. Moreover, opinions from those local social groups are extracted from the retrieved results, presented in the summaries and highlighted in the news web pages. We evaluate LocalSavvy with a user study. The quantitative and qualitative analysis shows that news articles aggregated by LocalSavvy present relevant and distinct local opinions, which can be clearly perceived by the subjects.

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Today, online stores collect a lot of customer feedback in the form of surveys, reviews, and comments. This feedback is categorized and in some cases responded to, but in general it is underutilized – even though customer satisfaction is essential to the success of their business. In this paper, we introduce several new techniques to interactively analyze customer comments and ratings to determine the positive and negative opinions expressed by the customers. First, we introduce a new discrimination-based technique to automatically extract the terms that are the subject of the positive or negative opinion (such as price or customer service) and that are frequently commented on. Second, we derive a Reverse-Distance-Weighting method to map the attributes to the related positive and negative opinions in the text. Third, the resulting high-dimensional feature vectors are visualized in a new summary representation that provides a quick overview. We also cluster the reviews according to the similarity of the comments. Special thumbnails are used to provide insight into the composition of the clusters and their relationship. In addition, an interactive circular correlation map is provided to allow analysts to detect the relationships of the comments to other important attributes and the scores. We have applied these techniques to customer comments from real-world online stores and product reviews from web sites to identify the strength and problems of different products and services, and show the potential of our technique.