Don’t Count, Predict! An Automatic Approach to Learning Sentiment Lexicons for Short Text. – NLPIR自然语言处理与信息检索共享平台

自然语言处理与信息检索共享平台 自然语言处理与信息检索共享平台

Don’t Count, Predict! An Automatic Approach to Learning Sentiment Lexicons for Short Text.

NLPIR SEMINAR Y2019#30

INTRO

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.

Arrangement

Tomorrow’s seminar is organized as follows:

  1. The seminar time is 1.pm, Mon (September 16, 2019), at Zhongguancun Technology Park ,Building 5, 1306.
  2. Baohua Zhang is going to give a presentation, the paper’s title is Don’t Count, Predict! An Automatic Approach to Learning Sentiment Lexicons for Short Text.
  3. Yaofei Yang will give a lecture of his paper.
  4. The seminar will be hosted by Ziyu Liu.

Everyone interested in this topic is welcomed to join us.
The following is the abstract of the paper.

Don’t Count, Predict! An Automatic Approach to Learning Sentiment Lexicons for Short Text

Duy Tin Vo and Yue Zhang

Abstract

We 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.

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