AUTOMATICALLY CONSTRUCTING TRAINING SETS FOR ELECTRONIC SENTIMENT ANALYSIS

摘要

Training data for training a neural network usable for electronic sentiment analysis can be automatically constructed. For example, an electronic communication usable for training the neural network and including multiple characters can be received. A sentiment dictionary including multiple expressions mapped to multiple sentiment values representing different sentiments can be received. Each expression in the sentiment dictionary can be mapped to a corresponding sentiment value. An overall sentiment for the electronic communication can be determined using the sentiment dictionary. Training data usable for training the neural network can be automatically constructed based on the overall sentiment of the electronic communication. The neural network can be trained using the training data. A second electronic communication including an unknown sentiment can be received. At least one sentiment associated with the second electronic communication can be determined using the neural network.

主权项

1. A non-transitory computer readable medium comprising program code executable by a processor for causing the processor to:
receive an electronic communication usable for training a neural network and comprising a plurality of characters; receive a sentiment dictionary comprising a plurality of expressions mapped to a plurality of sentiment values representing different sentiments, each expression of the plurality of expressions mapped to a corresponding sentiment value of the plurality of sentiment values; determine an overall sentiment for the electronic communication using the sentiment dictionary; automatically construct training data usable for training the neural network based at least in part on the overall sentiment of the electronic communication, wherein the training data comprises a plurality of overall sentiments associated with a plurality of electronic communications usable for training the neural network; train the neural network using the training data; receive a second electronic communication comprising an unknown sentiment; and determine at least one sentiment associated with the second electronic communication using the neural network.