Predicting the Semantic Orientation of Adjectives

We identify and validate from a large corpus constraints from conjunctions on the positive or negative semantic orientation of the conjoined adjectives. A log-linear regression model uses these constraints to predict whether conjoined adjectives are of same or different orientations, achieving 82% accuracy in this task when each conjunction is considered independently. Combining the constraints across many adjectives, a clustering algorithm separates the adjectives into groups of different orientations, and finally, adjectives are labeled positive or negative. Evaluations on real data and simulation experiments indicate high levels of performance: classification precision is more than 90% for adjectives that occur in a modest number of conjunctions in the corpus. 1 I n t r o d u c t i o n The semantic orientation or polarity of a word indicates the direction the word deviates from the norm for its semantic group or lezical field (Lehrer, 1974). It also constrains the word's usage in the language (Lyons, 1977), due to its evaluative characteristics (Battistella, 1990). For example, some nearly synonymous words differ in orientation because one implies desirability and the other does not (e.g., simple versus simplisfic). In linguistic constructs such as conjunctions, which impose constraints on the semantic orientation of their arguments (Anscombre and Ducrot, 1983; Elhadad and McKeown, 1990), the choices of arguments and connective are mutually constrained, as illustrated by: The tax proposal was simple and well-received } simplistic but well-received *simplistic and well-received by the public. In addition, almost all antonyms have different semantic orientations3 If we know that two words relate to the same property (for example, members of the same scalar group such as hot and cold) but have different orientations, we can usually infer that they are antonyms. Given that semantically similar words can be identified automatically on the basis of distributional properties and linguistic cues (Brown et al., 1992; Pereira et al., 1993; Hatzivassiloglou and McKeown, 1993), identifying the semantic orientation of words would allow a system to further refine the retrieved semantic similarity relationships, extracting antonyms. Unfortunately, dictionaries and similar sources (theusari, WordNet (Miller et al., 1990)) do not include semantic orientation information. 2 Explicit links between antonyms and synonyms may also be lacking, particularly when they depend on the domain of discourse; for example, the opposition bearbull appears only in stock market reports, where the two words take specialized meanings. In this paper, we present and evaluate a method that automatically retrieves semantic orientation information using indirect information collected from a large corpus. Because the method relies on the corpus, it extracts domain-dependent information and automatically adapts to a new domain when the corpus is changed. Our method achieves high precision (more than 90%), and, while our focus to date has been on adjectives, it can be directly applied to other word classes. Ultimately, our goal is to use this method in a larger system to automatically identify antonyms and distinguish near synonyms. 2 O v e r v i e w o f O u r A p p r o a c h Our approach relies on an analysis of textual corpora that correlates linguistic features, or indicators, with 1 Exceptions include a small number of terms that are both negative from a pragmatic viewpoint and yet stand in all antonymic relationship; such terms frequently lexicalize two unwanted extremes, e.g., verbose-terse. 2 Except implicitly, in the form of definitions and usage examples.