A text emotion analysis method based on attention mechanism

A technology of sentiment analysis and attention, applied in text database clustering/classification, semantic analysis, unstructured text data retrieval, etc. It is not very effective to solve the problem of long-range dependence, etc., to achieve the effect of fast training process, reducing the number of parameters and training time

Active Publication Date: 2019-03-29
SUN YAT SEN UNIV
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Problems solved by technology

However, RNN is not computationally efficient and is not good at modeling long-term dependencies. It is not v...

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  • A text emotion analysis method based on attention mechanism
  • A text emotion analysis method based on attention mechanism
  • A text emotion analysis method based on attention mechanism

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Embodiment 1

[0039] Such as figure 1 As shown, the present invention is a text sentiment analysis method based on attention mechanism, which is a deep learning method. We are using the SemEval-2014Task 4 dataset, which includes two domain-specific datasets for laptops (Laptops) and restaurants (Restaurants), which contain more than 6K sentences and fine-grained aspect-level annotations, which are aspect-level A standard dataset for sentiment analysis. Both domain-specific datasets have two sub-datasets: training set, test set.

[0040] In previous methods, cyclic calculations such as LSTM are usually used to encode sentences and target words. Since RNN calculations cannot be parallelized, the ability to model long-range dependencies is limited. The present invention does not use recursion, but uses two different attention encoders for context modeling, mining rich introspective and interactive semantic information in word embeddings. So we propose a text sentiment analysis method based...

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Abstract

The invention discloses a text emotion analysis method based on attention mechanism, which comprises the following steps: 1, preprocessing text data; 2, constructing a vocabulary and constructing a word vector by using a GloVe model; Thirdly, the sentence vector is encoded by intrinsic attention, and the target word vector is encoded by interactive attention. The two encoded vectors are fused by GRU, and the fused representation is obtained after average pooling. Fourthly, according to the fusion representation, the abstract feature of context vector is obtained by point-by-point feed-forwardnetwork (FFN), and then the probability distribution of affective classification labels is calculated by full connection and Softmax function, and the classification result is obtained. Fifthly, the preprocessed corpus is divided into training set and test set, the parameters of the model are trained many times, and the model with the highest classification accuracy is selected to classify the affective tendencies. The method of the invention only uses the attention mechanism to model the text, and strengthens the understanding of the target word, so that the user can understand the emotionaltendency held by the specific target word in the text.

Description

technical field [0001] The invention relates to the field of text sentiment analysis, and more specifically, relates to a text sentiment analysis method based on an attention mechanism. Background technique [0002] In the Internet era of information explosion, social platforms such as social networks and instant messaging platforms have developed rapidly, becoming one of the important ways for network users to communicate and communicate, and also one of the largest information generation platforms on the Internet. Using the massive text data of social networks for sentiment analysis tasks will help provide more help in accurate product recommendations, criminal tracking, and public opinion monitoring and guidance. [0003] Target-level sentiment classification, which aims to determine the sentiment orientation of a sentence towards a specific target word, is a fine-grained sentiment analysis task that aims to determine the sentiment polarity (negative, neutral, or positive...

Claims

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Application Information

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IPC IPC(8): G06F17/27G06F16/35
CPCG06F40/30
Inventor 王甲海宋有伟
Owner SUN YAT SEN UNIV
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