Mining WordNet for fuzzy sentiment: Sentiment tag extraction from WordNet glosses

Fred Durao's picture
Publication Author: 
A. Andreevskaia
S. Bergler
Host Title: 
In Proceedings of EACL-06, 11th Conference of the European Chapter of the Association for Computational Linguistics.

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 identiļ¬ed 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.

Available @

Event Location: 
Trento, IT
Type of paper: 
Article in Proceedings (WS or C)
ACL Anthology