Enhancing ConceptNet-based Sentiment Dictionary using Active Learning with Maximal-impact Strategy
Date Issued
2014
Date
2014
Author(s)
Wu, Chi-En
Abstract
Sentiment analysis aims to analyze the sentiments behind natural language text. Most sentiment analysis methods rely on sentiment dictionaries to identify sentiments in text. Our previous work proposed a ConceptNet-based semi-supervised approach, which propagated sentiment values from seed concepts to other concepts in ConceptNet. However, the seed concepts are insufficient to propagate sentiment values in a larger graph, and collecting large numbers of annotated seed concepts can be expensive. In this work, we refine our previous method by adding an active learning component. We also modify our previous value propagation method to estimate certainty score for each concept''s sentiment value. Based on these certainty scores, two query strategies, maximal uncertainty (MU) and maximal impact (MI), are proposed for choosing which concepts to send for sentiment annotation. Our experiment shows that our proposed certainty estimation methods can discriminate certain concepts from uncertain ones. Also, we show that both MU and MI strategies outperform the ``random'' strategy. Furthermore, MI corrects more concepts than MU, since it considers both uncertainty and influence of concepts. We conclude that our proposed active learning component can improve the quality of existing sentiment dictionaries.
Subjects
情緒辭典
數值傳遞
主動學習
群眾外包
Type
thesis
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