tailieunhanh - Báo cáo khoa học: "Mining WordNet for Fuzzy Sentiment: Sentiment Tag Extraction from WordNet Glosses"

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 extracting sentiment-bearing adjectives from WordNet using the Sentiment Tag Extraction Program (STEP). We did 58 STEP runs on unique non-intersecting seed lists drawn from manually annotated list of positive and negative adjectives and evaluated the results against other manually annotated lists. The 58 runs were then collapsed into a single set of 7, 813 unique words. . | Mining WordNet for Fuzzy Sentiment Sentiment Tag Extraction from WordNet Glosses Alina Andreevskaia and Sabine Bergler Concordia University Montreal Quebec Canada andreev bergler @ 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 extracting sentiment-bearing adjectives from WordNet using the Sentiment Tag Extraction Program STEP . We did 58 STEP runs on unique non-intersecting seed lists drawn from manually annotated list of positive and negative adjectives and evaluated the results against other manually annotated lists. The 58 runs were then collapsed 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 negative 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 membership in the fuzzy category of sentiment the core adjectives which had the highest Net Overlap scores were identified most accurately both by STEP and by human 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 semantic tagging of phrases and texts rely on a list of words annotated with some lexical semantic features. 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 category as equal no element is more of a member than any other Edmonds 1999 . In this paper we challenge the applicability of this assumption to the semantic category of sentiment which consists of positive negative and neutral subcategories and present a dictionary-based Sentiment Tag Extraction Program STEP that we use to .