The present disclosure provides an emoji recommendation method and device, The emoji recommendation method may include: acquiring training sentences corresponding to emojis; clustering the training sentences to categories; according to a number of training sentences in each category, calculating a first probability corresponding to each category; according, to correspondences between the training sentences and the emojis determining emojis included ifs each category, and calculating a second probability corresponding to each emoji in each category; according to occurrences of each word in each category, calculating a third probability corresponding to each word in each category; according to the first probability and the third probability, calculating a fourth probability of a target sentence belonging to each category; according to the fourth probabilities corresponding to the categories, determining a target category for the target sentence; and according to the second probability corresponding to each emoji in the target category, recommending an emoji.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. An emoji recommendation method, comprising: obtaining a plurality of training sentences corresponding to a plurality of emojis, each training sentence including one or more words; clustering the training sentences to obtain a plurality of interpretive categories; according to a total number of one or more training sentences included in each interpretive category, calculating a first probability corresponding to each interpretive category; according to correspondence relationships between the training sentences and the emojis, determining one or more emojis included in each interpretive category, and calculating a second probability corresponding to each emoji at each interpretive category; according to a number of occurrence of each word in each interpretive category, calculating a third probability corresponding to each word in each interpretive category; according to the first probability and the third probability, utilizing the Bayes formula to calculate a fourth probability of a target sentence inputted by a user belonging to each of the interpretive categories, wherein the target sentence includes one or more target words; according to the fourth probabilities corresponding, to the interpretive categories, determining a target interpretive category corresponding to the target sentence; and according to the second probability corresponding to each emoji included in the target interpretive category, recommending at least one emoji. 2. The method according to claim 1, wherein, according to the total number of the one or more training sentences included in each interpretive category, calculating the first probability corresponding to each interpretive category comprises: provided that i=1, 2, . . . , m, and m is a total number of the interpretive categories, counting the total number Nci of the training sentences included in each interpretive category Ci, respectively; and calculating the first probability P(Ci) corresponding to each interpretive category Ci by: P  ( C i ) = N C i ∑ l = 1 m   N C l . 3. The method according to claim 1, wherein, calculating the second probability corresponding to each emoji in each interpretive category comprises: provided that i=1, 2, . . . , m, m is the total number of the interpretive categories; k=1, 2, . . . , p, and p is a total number of the emojis. counting a number of occurrence Ni,ek of each emoji ek in each interpretive category Ci; and calculating the second probability P(ek|Ci) corresponding to each emoji ek in each interpretive category Ci by: P  ( e k  C i ) = N ie k ∑ l = 1 p   N ie l 4. The method according to claim 1, wherein, according to the number of occurrence of each word in each interpretive category, calculating the third probability corresponding to each word in each interpretive category comprises: provided that i=1, 2, . . . , m, m is the total number of the interpretive categories; q, and q is the total number of the words counting the number of occurrence Niwj of each word wj in each interpretive category Ci; and calculating the third probability P(wj|Ci) each word wj in each interpretive category Ci by: P  ( w j  C i ) = N iw j ∑ l = 1 q   N iw l . 5. The method according to claim 1, wherein, according to the fourth probabilities corresponding to the interpretive categories, determining the target interpretive category corresponding to the target sentence comprises: determining whether a maximum value of the fourth probabilities is greater than a first threshold; and when the maximum value of the fourth probabilities is greater than the first threshold, using the interpretive category corresponding to the maximum value of the fourth probabilities as the target interpretive category. 6. The method according to claim 1, wherein, according to the fourth probabilities corresponding to the interpretive categories, determining the, target interpretive category corresponding to the target sentence comprises: according to a descending order of the fourth probabilities, sorting the corresponding interpretive categories; according to the sorting result, calculating a sum of the fourth probabilities of first S interpretive categories, S being an integer greater than 1; determining whether a ratio of a maximum value of the fourth probabilities to the sum of the fourth probabilities of the first S interpretive categories is greater than a second threshold; and when the ratio is greater than the second threshold, using the interpretive cat-gory corresponding to the maximum value of the fourth probabilities as the target interpretive category. 7. The method according to claim 1, wherein, before according to the first probability and the third probability, utilizing the Bayes formula to calculate the fourth probability of the target sentence inputted by the user belonging to each of the interpretive categories, the method further comprising: determining whether a current input scenario is a preset scenario; and when the current input scenario is not the preset scenario, according to the first probability and the third probability, utilizing the Bayes Formula to calculate the fourth probability of the target sentence inputted by the user belonging to each of the interpretive categories. 8. The method according to claim 1, wherein, before calculating the third probability corresponding to each word in the corresponding interpretive category, the method further comprising at least one of the following steps: when the training sentence has a first language style, segmenting the training sentence; and filtering out at least one stopword for the words included in the training sentence. 9. An emoji recommendation device, comprising: a corpus training and a real-time recommendation unit; wherein the corpus training unit comprises; a clustering processing unit configured to: acquire a plurality of training sentences corresponding to a plurality of emojis, and cluster the training sentences to obtain a plurality of interpretive categories, wherein each training sentence includes or more words, a first calculation unit configured to, according to a total number of one ore training sentences included in each interpretive category, calculate a first probability corresponding to each interpretive category, a second calculation unit configured to, according to correspondence relationships between the training sentences and the emojis determine one or more emojis included n each interpretive category and calculate a second probability corresponding to each emoji in each interpretive category, and a third calculation configured to, according to a number of occurrence of each word in each interpretive category, calculate a third probability corresponding to each word in each interpretive category; the real-time recommendation unit comprises: a fourth calculation unit configure to, according to the first probability and the third probability, utilize the Bayes formula to calculate a fourth probability of a target sentence inputted by a user belonging to each of the interpretive categories, wherein the target sentence comprises one or more target words; a target category determination unit configured to, according to the fourth probabilities corresponding to the interpretive categories, determine a target interpretive category corresponding to the target sentence; and a real-time recommendation executing unit configured to, according to the second probability corresponding to each emoji included in the target interpretive category, recommend at least one emoji. 10. The device according to claim 9, wherein the target category determinate unit includes at least one of; a first determination unit configured to, determine whether a maximum value of the fourth probabilities is greater than a first threshold, and when the maximum value of the fourth probabilities is greater than the first threshold, use the interpretive category corresponding to the maximum value of the fourth probabilities as the target interpretive category and a second determination unit configured to, according to a descending order of the fourth probabilities, sort the corresponding interpretive categories, and according to a sorting result, calculate a sum of fourth probabilities of first S interpretive categories, S being an integer greater than 1, wherein the second determination unit is further configured to determine whether a ratio of the maximum value of the fourth probabilities to the sum of the fourth probabilities of first S interpretive categories is greater than a second threshold, and when the ratio is greater than the second threshold, use the interpretive category corresponding to the maximum Value of the fourth probability as the target interpretive category. 11. The device according to claim 9, wherein the real-time recommendation unit further comprises: a scenario determination unit configured to determine whether a current input scenario is a preset scenario, and when the current input scenario is not a present scenario, trigger the fourth calculation unit. 12. The device according to claim 9, wherein the corpus training unit further comprises at least one of: a segmentation unit configured to, before calculating the third probability corresponding to each word in the corresponding interpretive category, and when the training sentence has a first language style, segment the training sentence; and a stopword filtering unit configured to, before calculating the third probability corresponding to each word in the corresponding interpretive category, filter out at least one stopword for the words included in the training sentence.