Titlebook: Computational Linguistics and Intelligent Text Processing; 16th International C Alexander Gelbukh Conference proceedings 2015 Springer Inte

Homocystinuria

贝雷帽

forthy

Building Large Arabic Multi-domain Resources for Sentiment Analysisressing: the best performing classifiers and feature representation methods, the effect of introducing lexicon based features and factors affecting the accuracy of sentiment classification in general. All the datasets, experiments code and results have been made publicly available for scientific purposes.

脆弱吧

Learning Ranked Sentiment Lexiconstwo large datasets with 703,000 movie reviews and 189,000 hotel reviews showed that the proposed method outperforms the baselines while using a significantly lower dimensional lexicon than other methods.

inhibit

流利圆滑

Feature Selection for Twitter Sentiment Analysis: An Experimental Study results in subjectivity classification to the NRC state-of-the-art system with 4 million features that has ranked first in 2013 SemEval competition. Also, our selected features have shown a relative performance gain in the ensemble classification over the baseline of uni-gram and bi-gram features of 9.9% on CrowdScale and 11.9% on SemEval.

TSH582

An Iterative Emotion Classification Approach for Microblogsn results converge. Experimental results obtained by three different multi-label classifiers on NLP & CC2013 Chinese microblog emotion classification bakeoff dataset demonstrates the effectiveness of our iterative emotion classification approach.

恃强凌弱

entice

Inordinate