There has been a recent swell of interest in the automatic identification and extraction of opinions and emotions in text. In this paper, we present the first experimental results classifying the intensity of opinions and other types of subjectivity and classifying the subjectivity of deeply nested clauses. We use a wide range of features, including new syntactic features developed for opinion recognition. We vary the learning algorithm and the feature organization to explore the effect this has on the classification task. In 10-fold cross-validation experiments using support vector re-gression, we achieve improvements in mean-squared error over baseline ranging from 49 % to 51%. Using boosting, we achieve improvements in accuracy ranging from 23 % to 96%.

Abstract. In this paper we report about our research towards the use of affect in language wherein we have attempted to formalise the affective functionality at word and grammatical level for a fraction of Dutch and English. These formalisations have been demonstrated in a pilot experiment. The empirical background of the formalisation, and the results of the experiment constitute the basis for further research on a lexical, grammatical implementation of affect. 1

We present a lexicon-based approach to extracting sentiment from text. The Semantic Orientation CALculator (SO-CAL) uses dictionaries of words annotated with their semantic orientation (polarity and strength), and incorporates intensification and negation. SO-CAL is applied to the polarity classification task, the process of assigning a positive or negative label to a text that captures the text’s opinion towards its main subject matter. We show that SO-CAL’s performance is consistent across domains and in completely unseen data. Additionally, we describe the process of dictionary creation, and our use of Mechanical Turk to check dictionaries for consistency and reliability. 1.

This paper describes a method for ranking a large list of adjectives according to a subjectivity score without resorting to any knowledge-intensive external resources (such as lexical databases, parsers or manual annotation). The method only requires a list of adjectives to be ranked and a small set of "seeds" (manually selected subjective adjectives). The subjectivity score is obtained by computing the mutual information of pairs of adjectives taken from each set, using frequency and cooccurrence frequency counts on the World Wide Web, collected through queries to the AltaVista search engine. The obtained results improve significantly over a comparable low-resource acquisition algorithm.

We present a computational analysis of de re, de dicto, and de se belief and knowledge reports. Our analysis solves a problem first observed by Hector-Neri Casta~neda, namely, that the simple rule `(A knows that P ) implies P ' apparently does not hold if P contains a quasi-indexical. We present a single rule, in the context of a knowledge-representation and reasoning system, that holds for all P , including those containing quasi-indexicals. In so doing, we explore the difference between reasoning in a public communication language and in a knowledge-representation language, we demonstrate the importance of representing proper names explicitly, and we provide support for the necessity of considering sentences in the context of extended discourse (for example, written narrative) in order to fully capture certain features of their semantics. (This document is SUNY Buffalo Department of Computer Science Technical Report No. 95-49B, as well as SUNY Buffalo Center for Cognitive Science Tec...

This article explores recent research on language use in context, going beyond sentence boundaries and processing discourse---treating texts or dialogues as whole units composed of interrelated parts, not merely as sequences of isolated sentences. The article discusses the comprehension and production of language, looking at both texts and dialogues. A text to be processed might be, for example, a newspaper or magazine article being translated into another language or whose content is to be "understood" or abstracted in an information storage and retrieval system. A dialogue to be processed might be a conversation, spoken or typed, between a human and a computer, in service of some collaborative task. Many of the problems described here occur in both kinds of discourse. We use the words "speaker" and "writer," as well as "hearer" and "reader," almost interchangeably

This paper presents an annotation scheme for marking subjective content in meetings, specifically the opinions and sentiments that participants express as part of their discussion. The scheme adapts concepts from the Multi-perspective Question Answering (MPQA) Annotation Scheme (Wiebe et al., 2005; Wilson, 2008), an annotation scheme for marking opinions and attributions in the news. The adaptations reflect the differences in multiparty conversation as compared to text, as well as the overall goals of our project. 1.

Opinion mining is the task of extracting from a set of documents opinions expressed by a source on a specified target. This article presents a comparative study on the methods and resources that can be employed for mining opinions from quotations (reported speech) in newspaper articles. We show the difficulty of this task, motivated by the presence of different possible targets and the large variety of affect phenomena that quotes contain. We evaluate our approaches using annotated quotations extracted from news provided by the EMM news gathering engine. We conclude that a generic opinion mining system requires both the use of large lexicons, as well as specialised training and testing data.

News articles report on facts, events, and opinions with the intent of conveying the truth. However,

This paper describes work in developing probabilistic classifiers for a discourse segmentation problem that involves segmentation, reference resolution, and belief. Specifically, the problem is to segment a text into blocks such that all subjective sentences in a block are from the point of view of the same agent, and to identify noun phrases that refer to that agent. In our method for developing classifiers (Bruce & Wiebe 1994ab), rather than making assumptions about which variables to use and how they are related, statistical techniques are used to explore these questions empirically. Further, the types of models used in this work can express complex relationships among diverse sets of variables. This work is part of a large project that is in an early stage of development. The contributions of this paper are an illustration of framing a high-level discourse problem in such a way that it is amenable to statistical processing while still retaining its core, and a des...