1 research outputs found
Developing a concept-level knowledge base for sentiment analysis in Singlish
In this paper, we present Singlish sentiment lexicon, a concept-level
knowledge base for sentiment analysis that associates multiword expressions to
a set of emotion labels and a polarity value. Unlike many other sentiment
analysis resources, this lexicon is not built by manually labeling pieces of
knowledge coming from general NLP resources such as WordNet or DBPedia.
Instead, it is automatically constructed by applying graph-mining and
multi-dimensional scaling techniques on the affective common-sense knowledge
collected from three different sources. This knowledge is represented
redundantly at three levels: semantic network, matrix, and vector space.
Subsequently, the concepts are labeled by emotions and polarity through the
ensemble application of spreading activation, neural networks and an emotion
categorization model.Comment: CICLing 201