Title
Learning general connotation of words using graph-based algorithms
Abstract
In this paper, we introduce a connotation lexicon, a new type of lexicon that lists words with connotative polarity, i.e., words with positive connotation (e.g., award, promotion) and words with negative connotation (e.g., cancer, war). Connotation lexicons differ from much studied sentiment lexicons: the latter concerns words that express sentiment, while the former concerns words that evoke or associate with a specific polarity of sentiment. Understanding the connotation of words would seem to require common sense and world knowledge. However, we demonstrate that much of the connotative polarity of words can be inferred from natural language text in a nearly unsupervised manner. The key linguistic insight behind our approach is selectional preference of connotative predicates. We present graph-based algorithms using PageRank and HITS that collectively learn connotation lexicon together with connotative predicates. Our empirical study demonstrates that the resulting connotation lexicon is of great value for sentiment analysis complementing existing sentiment lexicons.
Year
Venue
Keywords
2011
EMNLP
sentiment lexicon,positive connotation,connotation lexicon,connotative predicate,general connotation,negative connotation,express sentiment,existing sentiment lexicon,graph-based algorithm,connotative polarity,sentiment analysis,resulting connotation lexicon
Field
DocType
Volume
Common sense,Computer science,Connotation,Artificial intelligence,Natural language processing,Empirical research,PageRank,Sentiment analysis,Algorithm,Lexicon,Natural language,Predicate (grammar),Linguistics
Conference
D11-1
Citations 
PageRank 
References 
13
0.78
22
Authors
3
Name
Order
Citations
PageRank
Song Feng128019.55
Ritwik Bose2131.11
Yejin Choi32239153.18