Paper Info
Title
Semi-automatic Building Method for a Multidimensional Affect Dictionary for a New Language
Abstract
Detecting the tone or emotive content of a text message is increasingly important in many natural language processing applications. Examples of such applications are rating new books or movies or products, judging the mood of a customer e-mail and routing it accordingly, measuring reputation that a person or a product has in the blogosphere. While for the English language there exists a number of affect, emotive, opinion, or affect computer-usable lexicons for automatically processing text, other languages rarely possess these primary resources. Here we present a semi-automatic technique for quickly building a multidimensional affect lexicon for a new language. Most of the work consists of defining 44 paired affect directions (e.g. love-hate, courage-fear, ... ) and choosing a small number of seed words for each dimension. From this initial investment, we show how a first pass affect lexicon can be created for new language, using a SVM classifier trained on a feature space produced from Latent Semantic Analysis over a large corpus in the new language. We evaluate the accuracy of placing newly found emotive words in one or more of the defined semantic dimensions. We illustrate this technique by creating an affect lexion for French, but the techniques can be applied to any language found on the Web and for which a large quantity of text exists.
Year
Venue
Keywords
2008
SIXTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION, LREC 2008
natural language processing,feature space,english language,latent semantic analysis,affective computing
Field
DocType
Citations 
Cache language model,Computer science,Speech recognition,Natural language,Lexicon,Language identification,Universal Networking Language,Natural language processing,Artificial intelligence,Semantic computing,Language technology,Semantic compression
Conference
7
PageRank 
References 
Authors
0.61
11
2
Name
Order
Citations
PageRank
Guillaume Pitel1768.39
Gregory Grefenstette21129147.00