Measuring praise and criticism: Inference of semantic orientation from association
ACM Transactions on Information Systems (TOIS)
Predicting the semantic orientation of adjectives
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Extracting semantic orientations of words using spin model
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Computing communities in large networks using random walks
ISCIS'05 Proceedings of the 20th international conference on Computer and Information Sciences
Identifying the semantic orientation of terms using S-HAL for sentiment analysis
Knowledge-Based Systems
| Hi-index | 12.05 |
Inferring the semantic orientation of subjective words (including adjectives, adverbs, nouns, and verbs) is an important task for sentiment analysis of texts. This paper proposes a novel algorithm, which attempts to attack this problem by optimizing the modularity of the word-to-word graph. Experimental results indicate that proposed method has two main advantages: (1) by spectral optimization of modularity, proposed approach displays a higher accuracy than other methods in inferring semantic orientation. For example, it achieves an accuracy of 88.8% on the HowNet-generated test set and (2) by effective usage of the global information, proposed approach is insensitive to the choice of paradigm words. In our experiment, only one pair of paradigm words is needed.