Apple Inc.
SENTIMENT PREDICTION FROM TEXTUAL DATA

Abstract:

Techniques for predicting sentiment from textual data are described herein. In some examples, the described techniques utilize a sentiment prediction model having bidirectional long short-term memory (LSTM) networks with one or more convolution-and-pooling stages. The bidirectional LSTM networks process vector representations of words in a textual word sequence to determine forward and backward word-level context feature vectors. Forward and backward phrase-level feature vectors are determined based on the forward and backward word-level context feature vectors. The one or more convolution-and-pooling stages pool the forward and backward phrase-level feature vectors to determine pooled phrase-level feature vectors. A sentiment representing the textual word sequence is determined based on the pooled phrase-level feature vectors.