CN109992780B - Specific target emotion classification method based on deep neural network - Google Patents
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Abstract
The invention provides a specific target emotion classification method based on a deep neural network. Belonging to the field of text sentiment classification of natural language processing. Firstly, Chinese word segmentation, stop word removal and punctuation removal are carried out on a data set, then word2vec algorithm is adopted to train the processed corpus to obtain corresponding word vectors, then the training set is input into a long-term and short-term memory network model structure based on a target attention mechanism, a specific target and a specific aspect are embedded in the process of realizing attention weight training, the specific target is expressed by weighted summation embedded in the specific aspect, so that the model gives more correct attention to the specific target and the specific aspect, the real semantics of the target is better captured, and finally the emotion classification accuracy of the specific target is improved.
Description
Technical Field
The invention relates to comment text emotion classification, in particular to a depth neural network-based specific target emotion classification method, and belongs to the technical field of natural language processing.
Background
The emotion analysis method mainly comprises a rule-based method, a machine learning-based method and a deep neural network-based method. The rule-based method usually needs to construct an emotion dictionary or emotion collocation template, and then calculates the emotion tendency of the text by comparing emotion words or fixed collocation contained in the comment text, but the construction of a relatively complete emotion dictionary or related collocation rules is a major problem at present. The method based on machine learning mainly carries out feature extraction and modeling on the labeled training corpus, thereby automatically realizing judgment of emotion polarity by using a machine learning algorithm; the method mainly comprises a support vector machine, naive Bayes, maximum information entropy, a conditional random field and the like, but the effect of machine learning classification is usually determined by the selection of features, great uncertainty exists in manual feature selection, and functions used in modeling the materials are generally simple, deep features are difficult to capture, and the modeling capability and generalization capability have great limitations. With the development of deep learning and the freedom and diversification of language expression modes, the advantage of the deep neural network technology is gradually highlighted and becomes the mainstream technology in the natural language processing field, compared with the emotion analysis method based on rules and the emotion analysis method based on machine learning, the deep neural network method can capture more comprehensive and deeper text characteristics when facing the current complex and changeable language phenomena due to the complexity of the model and the function, namely, the deep neural network method has better comprehension capability on the text and can achieve better effect in the emotion analysis field.
The LSTM neural network model, also called long-short term memory network model, is a variant of the RNN model. The LSTM solves the problem of information disappearance or information explosion when the RNN model transmits information in a long distance, and the LSTM neural network model adds various gate structures to neural network nodes on the basis of the RNN model to control the information to flow at different moments. In order to control the flow of information, a memory unit is specially designed in an internal node of the LSTM neural network, and the deletion or addition of the information is controlled through a gate structure, wherein the gate is a method for selectively passing the information, and the nodes of the LSTM neural network are provided with three gate structures for protecting and controlling the states of the nodes, namely an input gate, a forgetting gate and an output gate. The attention mechanism is derived from the fact that more attention is allocated to key parts of things concerned by human brains, is applied to the field of visual images at first and then applied to a task of natural language processing, and has a good effect.
The specific target emotion analysis is an important subtask of emotion analysis and is deeper emotion analysis. Different from ordinary emotion analysis, the judgment of the emotion polarity of the specific target not only depends on the context information of the text, but also depends on the feature information of the specific target. For example, the sentence "the food at this restaurant is very good to eat but expensive, but the service is compelling," the "taste" aspect for the target "food" is a positive emotion, the "price" aspect for the target "food" is a negative emotion, and the "service" aspect for the target "restaurant" is a positive emotion. Therefore, even if the same sentence is used, the completely opposite emotional polarities may occur for the same target, and different targets may have different emotional polarities. However, most text emotion classification models based on neural networks do not pay correct attention to the emotion of a specific aspect of a specific target, and the classification effect is poor. The method realizes better capture of the real semantics of the target, enriches the semantic information in the text, improves the accuracy of emotion classification of the specific target, and is the main research direction of the method.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a specific target emotion classification method based on a deep neural network and a target attention mechanism, which is used for analyzing the emotion colors of specific targets and specific aspects contained in text data in a social network.
The invention can be realized by adopting the following system:
a specific target emotion classification method based on a deep neural network is characterized by comprising the following steps:
step one, acquiring a Chinese emotion classification data set and preprocessing a text, and dividing the emotion classification data set into a training set and a test set;
secondly, training a word vector model for the preprocessed data set by using a word2vec tool and mapping texts in the data set into a word vector set;
step three, inputting the word vector set of the training set into the LSTM, discarding or transferring information by using three gates in the LSTM with trainable parameters, and outputting a series of hidden vectors h ═ { h ═ h1,h2,…,hn}；
Step four, putting the word vector matrix of the training set, the word vector matrix of the specific target and the word vector matrix of the specific aspect into a target attention mechanism to obtain each hiPositive weight p ofiSubsequently, a sentence representation Z is obtainedS；
Step five, generating a sentence ZSAnd judging the emotional polarity of the specific target by using the full connection layer and the softmax function.
Further, the text preprocessing specifically includes: :
preprocessing mainly comprises the steps of carrying out Chinese word segmentation, word stop and punctuation removal on the sentences marked with emotion polarities; 80% of the data sets were randomly selected as training sets and 20% as test sets.
Further, the training a word vector model on the preprocessed data set using a word2vec tool comprises:
after the word2vec model training is completed, the word2vec model may be used to map each word ω to a continuous feature vector eω∈RdWherein d represents the dimension of the word vector, and finally generating a word vector matrix E E ∈ Rv×dWhere V represents the size of the vocabulary in the dataset.
Further, the set of word vectors of the training set is input into the LSTM, information is discarded or passed using three gates in the LSTM with trainable parameters, and a series of hidden vectors h are output＝{h1,h2,…,hnThe method specifically comprises the following steps:
three gates in the LSTM include an input gate, a forgetting gate, and an output gate. Let xtFor input at t moment of certain node of LSTM neural network, htIs the output at time t, WxFor inputting the corresponding weight, WhIn order to output the corresponding weight, the flow of updating the LSTM neural network model through the gate structure control information is divided into the following steps:
calculating the value i at the moment t of the input gatetThe input gate controls the influence of the current input on the state value of the memory unit, and the calculation method is as follows
it＝sigmoid(Wxixt+Whiht-1+Wcict-1+bi) (1)
Calculating the value f at the moment of forgetting to leave the door ttThe influence of the historical information on the state value of the memory unit is controlled by the forgetting gate, and the calculation method is as follows:
ft＝sigmoid(Wxfxt+Whfht-1+Wcfct-1+bf) (2)
calculating the value of the candidate memory unit at the current time
And updating the value of the memory cell at the current moment, wherein the calculation method comprises the following steps:
ct＝ft·ct-1+it·ct (4)
finally calculating the output information h at the moment ttThe information is determined by an output gate, and the calculation method is as follows:
ot＝sigmoid(Wxoxt+Whoht-1+Wcoct-1+bo) (5)
ht＝ot·tanh(ct) (6)
putting the word vector matrix of the training set, the word vector matrix of the specific target and the word vector matrix of the specific aspect into a target attention mechanism specifically comprises the following steps:
and calculating the word vector matrix of the training set and the word vector matrix of the specific target as follows:
where the representative Average returns the Average of the input vectors.
Is a matrix of word vectors for a particular target,
as a word vector matrix of the training set, cSIs operative to capture both target information and context information.
Computing a weight vector q over all k aspect-specific embeddingstThe formula is as follows:
qt＝softmax(Wt·cS+bt) (8)
wherein q istRepresenting a vector of weights embedded over all k particular aspects, each weight qtIndicating the likelihood that a particular object belongs to a relevant aspect, WtAnd btRespectively representing the weight matrix and the bias vector.
Computing a target-specific vector tsThe formula is as follows
ts＝T·qt (9)
Wherein t issVector representing a specific target, T represents a word vector matrix of a specific aspect, T ∈ RK×dWherein K represents a specific aspect number.
Computing a positive weight piThe formula is as follows:
wherein
Wa∈Rd×dIs a trainable weight matrix.
Computing a sentence representation ZSThe formula is as follows:
each hidden vector hiCorresponding to a positive weight piWherein the value piCalculated from the target attention model, piCan be interpreted as ω when determining the emotional polarity of a specific target aiIs the probability that the model is correctly focused on the word. ZSRepresenting sentences used for emotion classification.
In summary, the invention provides a method for classifying specific target emotion based on a deep neural network. Belonging to the field of text emotion classification of natural language processing. Firstly, Chinese word segmentation, stop word removal and punctuation removal are carried out on a data set, then word2vec algorithm is adopted to train the processed corpus to obtain corresponding word vectors, then the training set is input into a long-term and short-term memory network model structure based on a target attention mechanism, a specific target and a specific aspect are embedded in the process of realizing attention weight training, the specific target is expressed by weighted summation embedded in the specific aspect, so that the model gives more correct attention to the specific target and the specific aspect, the real semantics of the target is better captured, and finally the emotion classification accuracy of the specific target is improved.
Compared with the prior art, the invention has the following beneficial effects:
on the basis of a long-term and short-term memory network, a target attention mechanism is introduced, a specific target is represented by a weighted sum embedded in a specific aspect, so that the model can better capture the real semantics of the specific target, the model can give more correct attention to the specific target and the specific aspect, meanwhile, the influence of secondary information in a text is ignored or reduced, the real semantics of the target can be better captured, and finally, the emotion classification accuracy of the specific target is improved.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments described in the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flow chart of the emotion classification model training of the present invention;
FIG. 2 is a view of the internal structure of the LSTM of the present invention;
FIG. 3 is the overall architecture of the emotion classification model of the present invention.
Detailed Description
In order to make the technical scheme in the embodiments of the present invention better understood and make the above objects, features and advantages of the present invention more obvious and understandable, the present invention provides an embodiment of a method for classifying specific target emotions based on a deep neural network, and the technical scheme of the present invention is further described in detail below with reference to the accompanying drawings:
the invention firstly provides a specific target emotion classification method based on a deep neural network, which comprises the following steps of:
s101, step 1: acquiring a Chinese emotion classification data set and preprocessing a text, and dividing the data set into a training set and a test set;
s102, step 2: training a word vector model for the preprocessed data set by using a word2vec tool and mapping texts in the data set into a word vector set;
s103, step 3: the word vector set of the training set is input into the LSTM, information is discarded or passed using three gates in the LSTM with trainable parameters, and a series of hidden vectors h ═ h is output1,h2,…,hn}；
S104, step 4: putting the word vector matrix of the training set, the word vector matrix of the specific target and the word vector matrix of the specific aspect into a target attention mechanism to obtain each hiPositive weight p ofiThen, the sentence Z is obtainedS；
S105, step 5: according to the generated sentence ZSAnd judging the emotional polarity of the specific target by using the full connection layer and the softmax function.
The preprocessing in the step 1 mainly comprises the steps of carrying out Chinese word segmentation, word stop and punctuation removal on the sentences marked with emotion polarities; 80% of the data sets were randomly selected as training sets and 20% as test sets.
After the word2vec model training is completed in step 2, the word2vec model may be used to map each word ω to a continuous feature vector eω∈RdWherein d represents the dimension of the word vector, and finally generating a word vector matrix E E ∈ Rv×dWhere V represents the size of the vocabulary in the dataset.
The three gates in the LSTM in step 3, as shown in fig. 2, include an input gate, a forgetting gate, and an output gate. Let xtFor input at t moment of certain node of LSTM neural network, htIs the output at time t, WxFor inputting the corresponding weight, WhIn order to output the corresponding weight value, the flow of updating the LSTM neural network model through the gate structure control information is to firstly calculate the value i of the input gate at the t momenttThe input gate controls the influence of the current input on the state value of the memory unit, and the calculation method is as follows
it＝sigmoid(Wxixt+Whiht-1+Wcict-1+bi) (1)
Then calculating the value f of the moment t of forgetting to leave the doortThe influence of the historical information on the state value of the memory unit is controlled by the forgetting gate, and the calculation method is as follows:
ft＝sigmoid(Wxfxt+Whfht-1+Wcfct-1+bf) (2)
recalculating current time candidate memory listValue of element
And updating the value of the memory cell at the current moment, wherein the calculation method comprises the following steps:
ct＝ft·ct-1+it·ct (4)
finally calculating the output information h at the moment ttThe information is determined by an output gate, and the calculation method is as follows:
ot＝sigmoid(Wxoxt+Whoht-1+Wcoct-1+bo) (5)
ht＝ot·tanh(ct) (6)
in step 4, the word vector matrix of the training set and the word vector matrix of the specific target are first averaged, as shown in the following formula:
where Average represents the Average of the returned input vectors.
Is a matrix of word vectors for a particular target,
as a word vector matrix of the training set, cSIs operative to capture both target information and context information.
Next step cSPut into the softmax function to calculate the weight vector q embedded on all k specific aspectstThe formula is as follows:
qt＝softmax(Wt·cS+bt) (8)
wherein q istRepresenting a vector of weights embedded over all k particular aspects, each weight qtIndicating the likelihood that a particular object belongs to a relevant aspect, WtAnd btRespectively representing the weight matrix and the bias vector.
Q is to betPerforming point multiplication with the word vector matrix of a specific aspect to calculate a vector t of a specific targetsThe formula is as follows
ts＝T·qt (9)
Wherein t issVector representing a specific target, T represents a word vector matrix of a specific aspect, T ∈ RK×dWhere K represents a specific number of facets, much smaller than V.
Next, a positive weight p is calculatediThe formula is as follows:
wherein
Wa∈Rd×dIs a trainable weight matrix.
Then the sentence representation Z is calculatedSThe formula is as follows:
each hidden vector hiCorresponding to a positive weight piWherein the value piCalculated from the target attention model, piCan be interpreted as ω when judging the emotion polarity of a specific target aiIs the probability that the model is correctly focused on the word. ZSRepresenting sentences used for emotion classification.
Finally, according to the generated sentence ZSThe emotion polarity of the specific target is judged by using the full connection layer and the softmax function, and a specific calculation flow is shown in fig. 3.
In summary, the invention provides a method for classifying specific target emotion based on a deep neural network. Belonging to the field of text emotion classification of natural language processing. Firstly, Chinese word segmentation, stop word removal and punctuation removal are carried out on a data set, then word2vec algorithm is adopted to train the processed corpus to obtain corresponding word vectors, then the training set is input into a long-term and short-term memory network model structure based on a target attention mechanism, a specific target and a specific aspect are embedded in the process of realizing attention weight training, the specific target is expressed by weighted summation embedded in the specific aspect, so that the model gives more correct attention to the specific target and the specific aspect, the real semantics of the target is better captured, and finally the emotion classification accuracy of the specific target is improved.
The above examples are intended to illustrate but not to limit the technical solutions of the present invention. Any modification or partial replacement without departing from the spirit and scope of the present invention should be covered in the claims of the present invention.
Claims (4)
1. A specific target emotion classification method based on a deep neural network is characterized by comprising the following steps:
step one, acquiring a Chinese emotion classification data set and preprocessing a text, and dividing the emotion classification data set into a training set and a test set;
secondly, training a word vector model for the preprocessed data set by using a word2vec tool and mapping texts in the data set into a word vector set;
step three, inputting the word vector set of the training set into the LSTM, discarding or transferring information by using three gates in the LSTM with trainable parameters, and outputting a series of hidden vectors h ═ { h { (h) }1,h2,…,hn}；
Step four, putting the word vector matrix of the training set, the word vector matrix of the specific target and the word vector matrix of the specific aspect into a target attention mechanism to obtain each hiPositive weight p ofiSubsequently, a sentence representation Z is obtainedS；
Step five, generating a sentence ZSJudging the emotion polarity of a specific target by using a full connection layer and a softmax function;
putting the word vector matrix of the training set, the word vector matrix of the specific target and the word vector matrix of the specific aspect into a target attention mechanism specifically comprises the following steps:
(5.1) calculating the word vector matrix of the training set and the word vector matrix of the specific target as follows:
where Average represents the Average of the returned input vectors,
is a matrix of word vectors for a particular target,
as a word vector matrix of the training set, cSFor capturing target information and context information simultaneously;
(5.2) computing a weight vector q embedded on all k specific aspectstThe formula is as follows:
qt＝soft max(Wt·cS+bt) (8)
wherein q istRepresenting a vector of weights embedded over all k particular aspects, each weight qtIndicating the likelihood that a particular object belongs to a relevant aspect, WtAnd btRespectively representing a weight matrix and an offset vector;
(5.3) calculating a target-specific vector tsThe formula is as follows
ts＝T·qt (9)
Wherein t issTarget-specific vector, T represents aspect-specific word vector matrix, T ∈ RK×dWherein K represents a specific aspect number;
(5.4) calculation ofPositive weight piThe formula is as follows:
wherein
Wa∈Rd×dIs a trainable weight matrix;
(5.5) calculating sentence representation ZSThe formula is as follows:
each hidden vector hiCorresponding to a positive weight piWherein the value piCalculated from the target attention model, piInterpreted as the probability, Z, of the word that the model is correctly interested in when judging the emotional polarity of a particular target aSRepresents a sentence for emotion classification;
in the process of realizing attention weight training, the specific target and the specific aspect are embedded, and the specific target is represented by the weighted sum of the embedding of the specific aspect, so that the emotion classification accuracy of the specific target is finally improved.
2. The method for classifying specific target emotion based on deep neural network as claimed in claim 1, wherein: the text preprocessing specifically comprises the following steps: carrying out Chinese word segmentation, word stop and punctuation removal on the sentences marked with emotion polarities; 80% of the data sets were randomly selected as training sets and 20% as test sets.
3. The method for classifying specific target emotion based on deep neural network as claimed in claim 1, wherein: training a word vector model using a word2vec tool on the preprocessed dataset comprises:
word2after the vec model training is completed, the word2vec model is used to map each word ω to a continuous feature vector eω∈RdWherein d represents the dimension of the word vector, and finally generating a word vector matrix E E ∈ Rv×dWhere V represents the size of the vocabulary in the dataset.
4. The method for classifying specific target emotion based on deep neural network as claimed in claim 1, wherein: the set of word vectors of the training set is input into the LSTM, information is discarded or passed using three gates in the LSTM with trainable parameters, and a series of hidden vectors h ═ { h ═ is output1,h2,…,hnThe method specifically comprises the following steps:
three gates in the LSTM, including an input gate, a forgetting gate and an output gate; let xtFor input at t moment of certain node of LSTM neural network, htIs the output at time t, WxFor inputting the corresponding weight, WhIn order to output the corresponding weight, the flow of updating the LSTM neural network model through the gate structure control information is divided into four steps:
(4.1) calculating the value i at the moment t of the input gatetThe input gate controls the influence of the current input on the state value of the memory unit, and the calculation method is as follows
it＝sigmoid(Wxixt+Whiht-1+Wcict-1+bi) (1)
(4.2) calculating the value f at the moment of forgetting to gate ttThe influence of the historical information on the state value of the memory unit is controlled by the forgetting gate, and the calculation method is as follows:
ft＝sigmoid(Wxfxt+Whfht-1+Wcfct-1+bf) (2)
(4.3) calculating the value of the candidate memory cell at the current time
And updating the value of the memory cell at the current time, calculatingThe method comprises the following steps:
ct＝ft·ct-1+it·ct (4)
(4.4) finally calculating the output information h at the moment ttThe information is determined by an output gate, and the calculation method is as follows:
ot＝sigmoid(Wxoxt+Whoht-1+Wcoct-1+bo) (5)
ht＝ot·tanh(ct) (6)。
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| CN108363753A (en) * | 2018-01-30 | 2018-08-03 | 南京邮电大学 | Comment text sentiment classification model is trained and sensibility classification method, device and equipment |
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| CN108717439A (en) * | 2018-05-16 | 2018-10-30 | 哈尔滨理工大学 | A kind of Chinese Text Categorization merged based on attention mechanism and characteristic strengthening |
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| CN107491490A (en) * | 2017-07-19 | 2017-12-19 | 华东师范大学 | Text sentiment classification method based on Emotion center |
| CN108460009A (en) * | 2017-12-14 | 2018-08-28 | 中山大学 | The attention mechanism Recognition with Recurrent Neural Network text emotion analytic approach of embedded sentiment dictionary |
| CN108363753A (en) * | 2018-01-30 | 2018-08-03 | 南京邮电大学 | Comment text sentiment classification model is trained and sensibility classification method, device and equipment |
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