Sentiment analysis seeks to identify the viewpoint(s) underlying a text span; an example application is classifying a movie review as "thumbs up" or "thumbs down". To determine this sentiment polarity, we propose a novel machine-learning method that applies text-categorization techniques to just the subjective portions of the document. Extracting these portions can be implemented using efficient techniques for finding minimum cuts in graphs; this greatly facilitates incorporation of cross-sentence contextual constraints.

An important part of our information-gathering behavior has always been to find out what other people think. With the growing availability and popularity of opinion-rich resources such as online review sites and personal blogs, new opportunities and challenges arise as people now can, and do, actively use information technologies to seek out and understand the opinions of others. The sudden eruption of activity in the area of opinion mining and sentiment analysis, which deals with the computational treatment of opinion, sentiment, and subjectivity in text, has thus occurred at least in part as a direct response to the surge of interest in new systems that deal directly with opinions as a first-class object. This survey covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems. Our focus is on methods that seek to address the new challenges raised by sentiment-aware applications, as compared to those that are already present in more traditional fact-based analysis. We include materialon summarization of evaluative text and on broader issues regarding privacy, manipulation, and economic impact that the development of opinion-oriented information-access services gives rise to. To facilitate future work, a discussion of available resources, benchmark datasets, and evaluation campaigns is also provided. 1

Abstract. This paper describes a corpus annotation project to study issues in the manual annotation of opinions, emotions, sentiments, speculations, evaluations and other private states in language. The resulting corpus annotation scheme is described, as well as examples of its use. In addition, the manual annotation process and the results of an inter-annotator agreement study on a 10,000-sentence corpus of articles drawn from the world press are presented.

... representation for encoding the opinions and perspectives expressed at any given point in a text. This paper evaluates the opinion annotation scheme for multi-perspective vs. factbased question answering using a new question and answer corpus.

We have introduced a search engine that can extract opinion sentences relevant to an open-domain query from Japanese blog pages. The engine identifies opinions based not only on positive or negative measurements but also on neutral opinions, requests, advice, and thoughts. To retrieve a number of opinion sentences that a user could reasonably be expected to read, we attempted to extract only explicitly stated writer's opinions at the sentence-level and to exclude quoted or implicational opinions. In our search engine, opinion sentences are identified based on features such as opinion clue expressions, and then, the relevance to the query of each identified opinion sentence is checked. The experimental results for various topics, obtained by comparing the output of the proposed opinion search engine with that of human judgments as to whether the sentences were opinions, showed that the proposed engine has promise as a practical application. 1

Opinion retrieval aims to tell if a document is positive, neutral or negative on a given topic. Opinion extraction further identifies the supportive and the non-supportive evidence of a document. To evaluate the performance of proposed technologies, a suitable corpus is necessary for opinionated tasks. This paper defines the annotations for opinionated materials. Heterogeneous experimental materials are annotated, and the agreements among annotators are analyzed. How human can monitor opinions of the whole is also examined. The corpus can be employed to opinion extraction, opinion summarization, opinion tracking and opinionated question answering. 1.

Question answering systems provide an elegant way for people to access an underlying knowledge base. Humans are not only interested in factual questions but also interested in opinions. This paper deals with question analysis and answer passage retrieval in opinion QA systems. For question analysis, six opinion question types are defined. A two-layered framework utilizing two question type classifiers is proposed. Algorithms for these two classifiers are described. The performance achieves 87.8 % in general question classification and 92.5 % in opinion question classification. The question focus is detected to form a query for the information retrieval system and the question polarity is detected to retain relevant sentences which have the same polarity as the question. For answer passage retrieval, three components are introduced. Relevant sentences retrieved are further identified whether the focus (Focus Detection) is in a scope of opinion (Opinion Scope Identification) or not, and if yes, whether the polarity of the scope matches with the polarity of the question (Polarity Detection). The best model achieves an F-measure of 40.59 % using partial match at the level of meaningful unit. With relevance issues removed, the F-measure of the best model boosts up to 84.96%. 1

The opinion analysis task is a pilot study task in NTCIR-6. It contains the challenges of opinion sentence extraction, opinion polarity judgment, opinion holder extraction and relevance sentence extraction. The three former are new tasks, and the latter is proven to be tough in TREC. In this paper, we introduce our system for analyzing opinionated information. Several formulae are proposed to decide the opinion polarities and strengths of words from composed characters and then further to process opinion sentences. The negation operators are also taken into consideration in opinion polarity judgment, and the opinion operators are used as clues to find the locations of opinion holders. The performance of the opinion extraction and polarity judgment achieves the f-measure 0.383 under the lenient metric and 0.180 under the strict metric, which is the second best of all participants.

representation for encoding the opinions and perspectives expressed at any given point in a text. This paper evaluates the opinion annotation scheme for multi-perspective vs. factbased question answering using a new question and answer corpus.

In this paper we present a detailed scheme for annotating expressions of opinions, beliefs, emotions, sentiment and speculation (private states) in the news and other discourse. We explore inter-annotator agreement for individual private state expressions, and show that these low-level annotations are useful for producing higher-level subjective sentence annotations.