Mining WordNet for a Fuzzy Sentiment: Sentiment Tag Extraction from WordNet Glosses

Mining WordNet for Fuzzy Sentiment: Sentiment Tag Extraction from WordNet Glosses Alina Andreevskaia and Sabine Bergler Concordia University Montreal, Quebec, Canada {andreev, bergler}@encs.concordia.ca Abstract Many of the tasks required for semantic tagging of phrases and texts rely on a list of words annotated with some semantic features. We present a method for ex- tracting sentiment-bearing adjectives from WordNet using the Sentiment Tag Extrac- tion Program (STEP). We did 58 STEP runs on unique non-intersecting seed lists drawn from manually annotated list of positive and negative adjectives and evalu- ated the results against other manually an- notated lists. The 58 runs were then col- lapsed into a single set of 7, 813 unique words. For each word we computed a Net Overlap Score by subtracting the total number of runs assigning this word a neg- ative sentiment from the total of the runs that consider it positive. We demonstrate that Net Overlap Score can be used as a measure of the words degree of member- ship in the fuzzy category of sentiment: the core adjectives, which had the high- est Net Overlap scores, were identified most accurately both by STEP and by hu- man annotators, while the words on the periphery of the category had the lowest scores and were associated with low rates of inter-annotator agreement. 1 Introduction Many of the tasks required for effective seman- tic tagging of phrases and texts rely on a list of words annotated with some lexical semantic fea- tures. Traditional approaches to the development of such lists are based on the implicit assumption of classical truth-conditional theories of meaning representation, which regard all members of a cat- egory as equal: no element is more of a mem- ber than any other (Edmonds, 1999). In this pa- per, we challenge the applicability of this assump- tion to the semantic category of sentiment, which consists of positive, negative and neutral subcate- gories, and present a dictionary-based Sentiment Tag Extraction Program (STEP) that we use to generate a fuzzy set of English sentiment-bearing words for the use in sentiment tagging systems 1. The proposed approach based on the fuzzy logic (Zadeh, 1987) is used here to assign fuzzy sen- timent tags to all words in WordNet (Fellbaum, 1998), that is it assigns sentiment tags and a degree of centrality of the annotated words to the senti- ment category. This assignment is based on Word- Net glosses. The implications of this approach for NLP and linguistic research are discussed. 2 The Category of Sentiment as a Fuzzy Set Some semantic categories have clear membership (e.g., lexical fields (Lehrer, 1974) of color, body parts or professions), while others are much more difficult to define. This prompted the development of approaches that regard the transition from mem- bership to non-membership in a semantic category as gradual rather than abrupt (Zadeh, 1987; Rosch, 1978). In this paper we approach the category of sentiment as one of such fuzzy categories where some words — such as good, bad — are very cen- …

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