Short text emotion factor extraction method and device based on deep learning
A deep learning, short text technology, applied in the field of machine translation, which can solve the problems of poor object recognition results and low natural language in emotional evaluation
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Embodiment 1
[0023] Embodiment 1, a method for extracting emotional elements from short texts based on deep learning, such as figure 1 As shown, including: using a bidirectional long-short-term memory cycle neural network to model sentences, and then constructing a classifier for each category to classify; for the input sentence, each word in the sentence is represented as a word vector, Input into the recurrent neural network as an input sequence; calculate each hidden state in the recurrent neural network in turn, and calculate the feature representation of the sentence; after obtaining the feature representation of the sentence, use a logical classifier to classify the sentence and identify the sentence The categories of sentiment elements reviewed in . In the model, the sentence is modeled directly using the state of the hidden layer.
[0024] A short text emotional element extraction method based on deep learning, based on the deep learning method of neural network, can automatically...
Embodiment 2
[0025] Embodiment two, a method for extracting emotional elements of short texts based on deep learning, such as Figure 5-7 Shown, on the basis of embodiment one. Further includes:
[0026] better, such as Figure 5 As shown, the sequential calculation of each hidden state in the cyclic neural network, specifically, the calculation method of the hidden layer node at the tth moment is as follows, Among them, h t f is the hidden node value of the forward recurrent neural network, h t b is the hidden node value of the backward cyclic neural network, and the hidden node value at the last moment is selected as the vector representation of the sentence, that is Where c is the required sentence vector representation, and the colon represents vector splicing. In the present invention, this strategy is denoted as brnn-final. This method is one of the most straightforward strategies to obtain the overall representation of the sentence. Use the last moment state to capture al...
Embodiment 3
[0086] Embodiment three, a device for extracting emotional elements from short texts based on deep learning, such as Figure 8 As shown, it includes: a modeling unit, which is used to model sentences with a bidirectional long-short-term memory recurrent neural network, and then constructs a classifier for each category to classify; an input unit is used for input sentences. Each word in the sentence is expressed as a word vector, which is input into the cyclic neural network as an input sequence; the calculation unit is used to calculate each hidden state in the cyclic neural network in turn, and calculates the feature representation of the sentence; the classification unit , after obtaining the feature representation of the sentence, a logical classifier is used to classify the sentence and identify the category of the emotional element commented in the sentence.
[0087] A short text emotional element extraction device based on deep learning, based on the deep learning metho...
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