Abstract
Online reviews are a feedback to the product and play a key role in improving the product to cater to
consumers. Online reviews that rely heavily on manual categorization are time consuming and labor
intensive.The recurrent neural network in deep learning can process time series data, while the long and
short term memory network can process long time sequence data well. This has good experimental
verification support in natural language processing, machine translation, speech recognition and language
model.The merits of the extracted data features affect the classification results produced by the classification
model. The LDA topic model adds a priori a posteriori knowledge to classify the data so that the
characteristics of the data can be extracted efficiently.Applied to the classifier can improve accuracy and
efficiency. Two-way long-term and short-term memory networks are variants and extensions of cyclic neural
networks.The deep learning framework Keras uses Tensorflow as the backend to build a convenient two-way
long-term and short-term memory network model, which provides a strong technical support for the
experiment.Using the LDA topic model to extract the keywords needed to train the neural network and
increase the internal relationship between words can improve the learning efficiency of the model. The
experimental results in the same experimental environment are better than the traditional word frequency
features.
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