Attention-based LSTM for Aspect-level Sentiment Classification

Aspect-level sentiment classification is a finegrained task in sentiment analysis. Since it provides more complete and in-depth results, aspect-level sentiment analysis has received much attention these years. In this paper, we reveal that the sentiment polarity of a sentence is not only determined by the content but is also highly related to the concerned aspect. For instance, "The appetizers are ok, but the service is slow.", for aspect taste, the polarity is positive while for service, the polarity is negative. Therefore, it is worthwhile to explore the connection between an aspect and the content of a sentence. To this end, we propose an Attention-based Long Short-Term Memory Network for aspect-level…

• NS
  National Science Foundation

  Award: 61332007
• NN
  National Natural Science Foundation of China

  Awards: 2012CB316301, 2013CB329403, 61272227/61332007, 61272227, 61332007
• TU
  Tsinghua University
• S
  Samsung
• BH
  Beijing Higher Education Young Elite Teacher Project
• NK
  National Key Research and Development Program of China

  Awards: 2012CB316301, 61332007, 2013CB329403