CN108197276B - Public emotion transfer distribution modeling method and device based on session - Google Patents
Public emotion transfer distribution modeling method and device based on session Download PDFInfo
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Abstract
The invention provides a conversation-based public emotion transfer distribution modeling method and device. The method comprises the following steps: acquiring a first preset number of pieces of session data in social media; carrying out emotion recognition on the first preset number of pieces of session data by using a Support Vector Machine (SVM) to obtain a second preset number of emotions; respectively marking the session data of the second preset number of emotions; performing probability statistics on the first preset number of pieces of session data based on the emotion transfer distribution tensor to obtain an emotion transfer probability table; and sampling the emotion transfer probability distribution according to a preset sampling algorithm to obtain an emotion transfer sampling sequence of the user. Therefore, the embodiment improves the maintainability of the system, is more suitable for machine learning, can guide conversation according to the emotion of the user, and improves the accuracy and robustness of the conversation.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a public emotion transfer distribution modeling method and device based on a session.
Background
The public conversation processing of the social platform in the related art mainly comprises the following schemes:
scheme one, finite state model. In the method, public conversation is regarded as a state transition sequence diagram, each node in the diagram represents an implicit conversation state and corresponds to system behavior (such as answers, queries, confirmations and the like), and state transition between nodes controls conversation flow. Finite state models are often applied to self-service voice service systems. In practical applications, the finite state model is simple, but lacks flexibility and is difficult to handle logic of complex dialogs.
Scheme two, Information State Update (ISU). In the method, all available information of a conversation process is modeled by using an 'information state', namely all information of conversation participants is integrated, and then public conversation behaviors are modeled. In practical applications, the ISU needs to integrate all useful information in the dialog, and the processing is not feasible.
In addition, the above two public conversation processing schemes require computational linguistics experts to design and write conversation schemes, which increases the design and development cost of the conversation system and reduces the maintainability of the system.
Disclosure of Invention
The embodiment of the invention provides a conversation-based public emotion transfer distribution modeling method and device, aiming at solving the defects in the related technology.
In a first aspect, the invention provides a conversation-based public sentiment transfer distribution modeling method, which comprises the following steps:
acquiring a first preset number of pieces of session data in social media;
carrying out emotion recognition on the first preset number of pieces of session data by using a Support Vector Machine (SVM) to obtain a second preset number of emotions;
respectively marking the session data of the second preset number of emotions;
carrying out probability statistics on the first preset number of pieces of session data to obtain an emotion transfer probability table and an emotion transfer tensor;
sampling the emotion transfer probability distribution according to a preset sampling algorithm to obtain an emotion transfer sampling sequence of the user; the emotion transition probability distribution is corresponding data in the emotion transition probability table.
Optionally, the conversation data includes a television drama speech, a movie speech, or a dialogue of a conversation website in a different language; the second predetermined number of emotions includes neutral, happy, surprised, sad and angry.
Optionally, the emotion transfer distribution tensor is expressed in the form of:
{ initial emotion, stimulated emotion, generated emotion, transition probability }.
Optionally, the preset sampling algorithm is a markov monte carlo sampling method.
Optionally, the sampling the emotion transfer probability distribution according to a preset sampling algorithm, and obtaining an emotion transfer sampling sequence of the user includes:
based on the initial quantity of the sampling session length, the session length is adjusted according to a preset step length, and the emotion transfer probability distribution is sampled according to a preset sampling algorithm, so that a plurality of emotion transfer sampling sequences of the user are obtained.
In a second aspect, the present invention provides a conversation-based public sentiment transfer distribution modeling apparatus, comprising:
the session data acquisition module is used for acquiring a first preset number of pieces of session data in the social media;
the emotion recognition module is used for carrying out emotion recognition on the first preset number of pieces of session data by using a Support Vector Machine (SVM) to obtain a second preset number of emotions;
the session data marking module is used for respectively marking the session data of the second preset number of emotions;
the emotion transfer probability statistics module is used for carrying out probability statistics on the first preset number of pieces of session data to obtain an emotion transfer probability table and an emotion transfer tensor;
the emotion transfer distribution sampling module is used for sampling emotion transfer probability distribution according to a preset sampling algorithm to obtain an emotion transfer sampling sequence of the user; the emotion transition probability distribution is corresponding data in the emotion transition probability table.
Optionally, the conversation data includes a television drama speech, a movie speech, or a dialogue of a conversation website in a different language; the second predetermined number of emotions includes neutral, happy, surprised, sad and angry.
Optionally, the emotion transfer distribution tensor is expressed in the form of:
{ initial emotion, stimulated emotion, generated emotion, transition probability }.
Optionally, the preset sampling algorithm is a markov monte carlo sampling method.
Optionally, the emotion transfer distribution sampling module includes:
and the sampling unit is used for adjusting the session length according to a preset step length based on the initial quantity of the sampling session length and sampling the emotion transfer probability distribution according to a preset sampling algorithm to obtain a plurality of emotion transfer sampling sequences of the user.
According to the technical scheme, a first preset number of pieces of session data in the social media are obtained firstly; secondly, carrying out emotion recognition on the first preset number of pieces of session data by using a Support Vector Machine (SVM) to obtain a second preset number of emotions; respectively marking the session data of the second preset number of emotions; then carrying out probability statistics on the first preset number of pieces of session data to obtain an emotion transfer probability table and an emotion transfer tensor; and finally, sampling the emotion transfer probability distribution according to a preset sampling algorithm to obtain an emotion transfer sampling sequence of the user. Therefore, in the embodiment, by acquiring the dialog of the unspecific user, the uncertainty table of emotion transfer of the person can be introduced, and the defect that the conversation database is excessively depended in the related technology can be overcome, so that the maintainability of the system is improved, and the method is more suitable for machine learning. In addition, in this embodiment, by constructing the emotion transfer distribution tensor of the user, the emotion distribution model of the user can be determined, that is, an emotion transfer sequence is generated, so that a conversation can be guided according to the emotion of the user, and the accuracy and robustness of a conversation are improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a schematic flow chart of a method for modeling conversation-based public sentiment transfer distribution according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart illustrating an emotion classification of session data by using an SVM according to an embodiment of the present invention;
FIG. 3 is a schematic diagram illustrating MCMC sampling principles according to an embodiment of the present invention;
FIG. 4 is a diagram illustrating a dialog corpus in accordance with an embodiment of the present invention;
FIG. 5 is a sampling sequence diagram of emotion transfer in conversation of "Laoyouji";
FIG. 6 is a sample sequence diagram of emotion transfer for a session of 15 movies;
FIG. 7 is a block diagram of a conversation-based public sentiment transfer distribution modeling apparatus according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a schematic flow chart of a method for modeling public sentiment transfer distribution based on a conversation according to an embodiment of the present invention. Referring to fig. 1, the method includes:
101, acquiring a first preset number of pieces of session data in social media;
102, carrying out emotion recognition on the first preset number of pieces of session data by using a Support Vector Machine (SVM) to obtain a second preset number of emotions;
103, respectively marking the session data of the second preset number of emotions;
104, carrying out probability statistics on the first preset number of pieces of session data to obtain an emotion transfer probability table and an emotion transfer tensor;
105, sampling the emotion transition probability distribution according to a preset sampling algorithm to obtain an emotion transition sampling sequence of the user
According to the technical scheme, the uncertainty table of emotion transfer of the human can be introduced by acquiring the dialog of the non-specific user, and the defect that the conversation database is excessively depended in the related technology can be overcome, so that the maintainability of the system is improved, and the method is more suitable for machine learning. In addition, in this embodiment, by constructing the emotion transfer distribution tensor of the user, the emotion distribution model of the user can be determined, that is, an emotion transfer sequence is generated, so that a conversation can be guided according to the emotion of the user, and the accuracy and robustness of a conversation are improved.
The following describes in detail the steps of the public sentiment transfer distribution modeling method provided by the embodiment of the present invention with reference to the accompanying drawings and embodiments.
First, a step of obtaining a first preset number of pieces of session data in social media is introduced 101.
The first preset number may be 1000, 10000, 100000, etc., and may be set by a person skilled in the art according to a specific scenario, which is not limited herein. In one embodiment, the first predetermined number is 22172.
The session data includes a question and an answer. For example, the question "do you eat", and the answer "i have eaten", which is one piece of session data.
In the embodiment of the invention, a crawler technology in the related technology is utilized to obtain a first preset number of pieces of session data from social media such as televisions, websites and the like. Wherein the conversation data can be television drama lines, movie lines or dialogs of conversation websites in different languages, etc.
Therefore, the method for obtaining the conversation data by using the crawler technology has randomness, and the conversation or speech of the social media is not specific to a specific user, so that the obtained conversation data can introduce the uncertainty representation of the emotion transfer of the human into the method, and is convenient for machine learning, thereby overcoming the defect that a computational linguistics expert designs or writes a conversation scheme, and improving the maintainability of the system.
Secondly, introducing 102, and performing emotion recognition on the first preset number of pieces of session data by using a Support Vector Machine (SVM) to obtain a second preset number of emotions.
The second preset number may be 3, 4, 5 or more, and may be set by those skilled in the art according to the specific situation. In one embodiment, the second predetermined number is 5, i.e. the second predetermined number of emotions may be neutral, happy, surprised, sad and angry.
In an embodiment of the present invention, a Support Vector Machine (SVM) is utilized to perform emotion recognition on the first preset number of pieces of session data, that is, each piece of session data corresponds to neutral, happy, surprised, sad or angry. The specific process of the SVM performing emotion classification on the session data is shown in FIG. 2. And (3) performing text vectorization on the session data with the 5 types of labels, performing feature selection, then calculating the weight (TF (T) IDF) of each feature, and finally performing model training and prediction to obtain a classification result of the session data.
Therefore, the emotion classification is carried out from the session data through the support vector machine SVM, and the emotion classification accuracy can be ensured.
Then, the step of marking the session data of the second preset number of emotions is introduced 103.
In order to facilitate subsequent quantitative calculation, the 5 emotions are replaced by the labels "0, 1,2, 3 and 4" in one embodiment of the invention, namely, the labels "0, 1,2, 3 and 4" are respectively marked as "neutral, happy, surprised, wounded and angry".
Then, introducing 104, and performing probability statistics on the first preset number of pieces of session data to obtain an emotion transfer probability table and an emotion transfer tensor.
The emotion transfer distribution tensor is expressed by the following form: { initial emotion, stimulated emotion, generated emotion, transition probability }. Wherein the options for initiating emotion, stimulating emotion, and generating emotion include: neutral, happy, surprised, hurting heart and engendering qi, the corresponding labels are: "0, 1,2, 3, 4". The meaning of the emotion transfer distribution tensor is: the initial emotion of the user is a certain emotion, and when the emotion of the user is stimulated by the certain emotion of the external interface, the emotion of the user is transferred to the probability value for generating the emotion.
In an embodiment, probability statistics is performed on a first preset number of pieces of session data, and then each piece of session data is sequentially used as an initial emotion, a stimulated emotion and a generated emotion, so that an emotion transition probability table of a second preset number x a second preset number can be obtained. In one embodiment, if the second predetermined number is 5, the size of the emotion transfer probability table is 5 × 5.
Finally, introducing 105, and sampling the emotion transfer probability distribution according to a preset sampling algorithm to obtain an emotion transfer sampling sequence of the user.
In this embodiment, the preset sampling algorithm may adopt a markov monte carlo sampling method, and the sampling process of the sampling method includes:
where 0.. t, t +1 is the time series at sampling, x0... is the sample series, and α (i, j) represents the acceptance rate from state i to state j, the physical meaning being understood as accepting this transition with the probability of α (i, j) when jumping from state i to state j with the probability of q (i, j) on the original mahalanobis chain.
As shown in fig. 3, the algorithm of the markov monte carlo sampling method is as follows:
assuming that there is a transition matrix Q (corresponding to Q (i, j)), the initial state of the mahalanobis chain refers to the probability distribution, e.g., p ═ 0.2,0.1,0.3,0.2, 0.2) and then is sampled according to the following algorithm:
the first step is to randomly select an initial state xt, such as state 1 (representing the happy emotion), then sample from the next time t +1 to y, the state of y is determined according to the polynomial distribution sampling of probability transition, and then it needs to judge whether to accept the sample y, i.e. a value u (between 0 and 1) is adopted from the binomial distribution to compare with an acceptance rate alpha, the value of alpha is equal to p (y), q (xt | y). If u is less than alpha, accepting the transition from Xt to y, namely assigning the value of y to Xt +1, otherwise not accepting the transition, and Xt +1 is still the state corresponding to Xt. In the experimental process, a convergence limit L can be set, namely, the result of sampling for L times is not changed, namely, the sampling is received, and the representativeness of the sampling sequence is enhanced. This results in a sample sequence: for example, 1,2,0,0,1,1,0,1 (assuming that we set the sampling length to be 8), this is a transfer sequence representing emotion, happy- > surprised- > neutral.
In the embodiment, the emotion transfer probability distribution is sampled by adopting a Markov Monte Carlo sampling method, so that the emotion transfer sampling sequence of the user can be obtained. In an embodiment, obtaining the emotion transfer sample sequence may further include:
based on the initial quantity of the sampling session length, the session length is adjusted according to a preset step length, and the emotion transfer probability distribution is sampled according to a preset sampling algorithm, so that a plurality of emotion transfer sampling sequences of the user are obtained.
Therefore, in the embodiment, by acquiring the dialog of the unspecific user, the uncertainty of emotion transfer of the person can be introduced, and the defect that the conversation database is excessively depended in the related technology can be overcome, so that the maintainability of the system is improved, and the method is more suitable for machine learning. In addition, in the embodiment, through a statistical method, the emotion distribution model of the user can be determined, that is, the emotion transfer sequence is generated, so that the dialogue can be guided according to the emotion of the user, and the accuracy and robustness of the conversation are improved.
Example one
In this embodiment, the drama "old friend note" is used as session data to generate emotion transfer distribution. First, 22172 sentences are collected for the session data in the "old friend" of the drama, where ∞ represents the end of a session. The session data is shown in fig. 4. And then, carrying out emotion classification on the session data by using the SVM, and respectively labeling 'neutral, happy, surprised, wounded and angry' on the emotion category of each session data by using labels '0, 1,2, 3 and 4'. The classification results are shown in table 1.
TABLE 1 conversational data sentiment classification in the American drama "Laoyouji
Then, the emotion transfer probability of the session data is calculated, and the emotion transfer of the session data is counted by a statistical method according to the emotion transfer distribution tensor, and the statistical results are shown in tables 2(a) to (e).
TABLE 2 Emotion transition probability of (a) - (e) Session data of American "Laoyouji
Finally, in this embodiment, different sampling session lengths are set: sampling session lengths are respectively 6, 8 and 10, an initial emotion is selected as [ impaired heart ], a stimulation emotion is selected as [ neutral, happy, surprised, impaired heart and angry ] to form five groups of emotion transfer probability distributions, and an MCMC sampling method is used to obtain an emotion transfer sequence of the drama 'friend' shown in figures 5(a) to (c).
In this way, in the embodiment, emotion transfer characteristics of session data of different social platforms and emotion transfer characteristics of different conversation lengths can be observed and analyzed.
Example two
In this embodiment, a movie speech is used as session data to generate emotion transfer distribution. First, the session data come from 15 more classical domestic and foreign movies, totaling 22302 lines of session lines. The movie names are shown in table 3.
TABLE 3 sources of session data
| Serial number | English name | Name of Chinese | |
| 1 | Me before you | Before you meet | |
| 2 | Pursuit Of Happyness | When happy to knock door | |
| 3 | Forrest.Gump | Am of great origin | |
| 4 | Life is beautiful | Beautiful life | |
| 5 | Pirates of the Caribbean | Sea robber of Caribbean | |
| 6 | The Truman Show | World of Chumen | |
| 7 | Once Upon a Time in America | Past in the United states | |
| 8 | La La land | Love city | |
| 9 | Hachiko：A Dog’s Story | Story of dog loyalty | |
| 10 | Titanic | Titanic number | |
| 11 | The great wall | Great wall | |
| 12 | Lost On Journey | Jiong for man | |
| 13 | To live | Is alive | |
| 14 | | Alvada A | |
| 15 | Before sunset | Before sunset |
And then, carrying out emotion classification on the session data by using the SVM, and respectively labeling 'neutral, happy, surprised, wounded and angry' on the emotion category of each session data by using labels '0, 1,2, 3 and 4'. The classification results are shown in tables 4(a) to (e).
TABLE 4 Emotion transition probabilities for film lines (a) - (e)
Finally, in this embodiment, different sampling session lengths are set: the lengths of sampling sessions are respectively 6, 8 and 10, five groups of emotion transfer probability distributions with initial emotions of [ impaired heart ], stimulating emotions of [ neutral, happy, surprised, impaired heart and angry ] are selected, and the emotion transfer sequences of 15 movies are obtained by using an MCMC sampling method and are shown in FIGS. 6(a) - (c).
In this way, in the embodiment, emotion transfer characteristics of session data of different social platforms and emotion transfer characteristics of different conversation lengths can be observed and analyzed.
FIG. 7 is a block diagram of a conversation-based public sentiment transfer distribution modeling apparatus according to an embodiment of the present invention. Referring to fig. 7, the apparatus includes:
a session data obtaining module 701, configured to obtain a first preset number of pieces of session data in social media;
an emotion recognition module 702, configured to perform emotion recognition on the first preset number of pieces of session data by using a support vector machine SVM, to obtain a second preset number of kinds of emotions;
a session data marking module 703, configured to mark session data of the second preset number of emotions respectively;
an emotion transfer probability statistics module 704, configured to perform probability statistics on the first preset number of pieces of session data to obtain an emotion transfer probability table and an emotion transfer tensor;
the emotion transfer distribution sampling module 705 is used for sampling emotion transfer probability distribution according to a preset sampling algorithm to obtain an emotion transfer sampling sequence of the user; the emotion transition probability distribution is corresponding data in the emotion transition probability table.
Optionally, the conversation data includes a television drama speech, a movie speech, or a dialogue of a conversation website in a different language; the second predetermined number of emotions includes neutral, happy, surprised, sad and angry.
Optionally, the emotion transfer distribution tensor is expressed in the form of:
{ initial emotion, stimulated emotion, generated emotion, transition probability }.
Optionally, the preset sampling algorithm is a markov monte carlo sampling method.
Optionally, the emotion transfer distribution sampling module includes:
and the sampling unit is used for adjusting the session length according to a preset step length based on the initial quantity of the sampling session length and sampling the emotion transfer probability distribution according to a preset sampling algorithm to obtain a plurality of emotion transfer sampling sequences of the user.
It should be noted that, the conversation-based public emotion transfer distribution modeling apparatus provided in the embodiment of the present invention is in a one-to-one correspondence relationship with the above method, and the implementation details of the above method are also applicable to the above apparatus, and the system will not be described in detail in the embodiment of the present invention.
In the description of the present invention, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
The above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present invention, and they should be construed as being included in the following claims and description.
Claims (8)
1. A conversation-based public sentiment transfer distribution modeling method is characterized by comprising the following steps:
acquiring a first preset number of pieces of session data in social media;
carrying out emotion recognition on the first preset number of pieces of session data by using a Support Vector Machine (SVM) to obtain a second preset number of emotions;
respectively marking the session data of the second preset number of emotions;
carrying out probability statistics on the first preset number of pieces of session data to obtain an emotion transfer probability table and an emotion transfer tensor;
sampling the emotion transfer probability distribution according to a preset sampling algorithm to obtain an emotion transfer sampling sequence of the user; the emotion transition probability distribution is corresponding data in the emotion transition probability table;
the emotion transfer sampling sequence of the user comprises:
based on the initial quantity of the sampling session length, the session length is adjusted according to a preset step length, and the emotion transfer probability distribution is sampled according to a preset sampling algorithm, so that a plurality of emotion transfer sampling sequences of the user are obtained.
2. The modeling method for public sentiment transfer distribution according to claim 1, wherein the conversation data includes a tv drama line, a movie line or a dialogue of conversation sites in different languages; the second predetermined number of emotions includes neutral, happy, surprised, sad and angry.
3. The modeling method of public emotional transition distribution according to claim 1, wherein the emotional transition distribution tensor is expressed in the form of:
{ initial emotion, stimulated emotion, generated emotion, transition probability }.
4. The modeling method of public sentiment transfer distribution according to claim 1, wherein the predetermined sampling algorithm is a markov monte carlo sampling method.
5. An apparatus for modeling public sentiment transfer distribution based on conversation, the apparatus comprising:
the session data acquisition module is used for acquiring a first preset number of pieces of session data in the social media;
the emotion recognition module is used for carrying out emotion recognition on the first preset number of pieces of session data by using a Support Vector Machine (SVM) to obtain a second preset number of emotions;
the session data marking module is used for respectively marking the session data of the second preset number of emotions;
the emotion transfer probability statistics module is used for carrying out probability statistics on the first preset number of pieces of session data to obtain an emotion transfer probability table and an emotion transfer tensor;
the emotion transfer distribution sampling module is used for sampling emotion transfer probability distribution according to a preset sampling algorithm to obtain an emotion transfer sampling sequence of the user; the emotion transition probability distribution is corresponding data in the emotion transition probability table;
the emotion transfer distribution sampling module comprises:
and the sampling unit is used for adjusting the session length according to a preset step length based on the initial quantity of the sampling session length and sampling the emotion transfer probability distribution according to a preset sampling algorithm to obtain a plurality of emotion transfer sampling sequences of the user.
6. The modeling apparatus for public emotional transfer distribution according to claim 5, wherein the conversational data includes a tv drama speech, a movie speech, or a dialogue of a different language conversational website; the second predetermined number of emotions includes neutral, happy, surprised, sad and angry.
7. The modeling apparatus for public emotional transition distribution according to claim 5, wherein the emotional transition distribution tensor is expressed in the form of:
{ initial emotion, stimulated emotion, generated emotion, transition probability }.
8. The public sentiment transfer distribution modeling apparatus of claim 5 wherein the predetermined sampling algorithm is a Markov Monte Carlo sampling method.
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