Everyone interested in this topic is welcomed to join us.
The following is the abstract of the paper.<\/p>\n\n\n\n

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\n\t\tDon\u2019t Count, Predict! An Automatic Approach to Learning Sentiment Lexicons for Short Text\n\t<\/p>\n\t

\n\t\tDuy Tin Vo and Yue Zhang\n\t<\/p>\n\t

\n\t\tAbstract\n\t<\/p>\n\t

\n\t\tWe describe an efficient neural network method to automatically learn sentiment lexicons without relying on any manual resources. The method takes inspiration from the NRC method, which gives the best results in SemEval13 by leveraging emoticons in large tweets, using the PMI between words and tweet sentiments to define the sentiment attributes of words. We show that better lexicons can be learned by using them to predict the tweet sentiment labels. By using a very simple neural network, our method is fast and can take advantage of the same data volume as the NRC method. Experiments show that our lexicons give significantly better accuracies on multiple languages compared to the current best methods.\n\t<\/p>\n<\/div>\n\n\n\n

Don\u2019t-Count-Predict-An-Automatic-Approach-to-Learning-Sentiment<\/a>\u4e0b\u8f7d<\/a><\/div>\n\n\n\n\n\n\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

INTRO<\/h3>\n\n\n\n In the new semester, our Lab, Web Search Mining and Security Lab, plans to hold an academic seminar every Monday, and each time a keynote speaker will share understanding of papers on his\/her related research with you.<\/p>\n\n\n\n

INTRO<\/h3>\n\n\n\n
In the new semester, our Lab, Web Search Mining and Security Lab, plans to hold an academic seminar every Monday, and each time a keynote speaker will share understanding of papers on his\/her related research with you.<\/p>\n\n\n\n