Described herein is a framework for classifying social media inputs. In accordance with one aspect of the framework, one or more social media inputs is acquired from one or more social media platforms. The social media inputs are cleaned to remove redundant elements. One or more features are extracted from the cleaned social media inputs. The social media inputs are classified by a trained classifier into predefined categories using the extracted one or more features.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method for classifying social media inputs comprising: acquiring one or more social media inputs from one or more social media platforms; cleaning the one or more social media inputs to remove redundant elements; extracting one or more features from the cleaned one or more social media inputs; and classifying, by a trained classifier, the social media inputs into predefined categories using the extracted one or more features. 2. The method of claim 1 comprising selecting the predefined categories. 3. The method of claim 1 wherein acquiring the one or more social media inputs comprises acquiring the social media inputs from the one or more social media platforms via an application programming interface (API). 4. The method of claim 1 wherein cleaning the one or more social media inputs comprises removing the redundant elements using one or more predefined filters. 5. The method of claim 1 wherein the redundant elements comprise emojis, special characters, Uniform Resource Locators (URLs), pictures, stop words and nouns. 6. The method of claim 1 wherein cleaning the one or more social media inputs comprises removing any stop words. 7. The method of claim 1 wherein cleaning the one or more social media inputs comprises removing any nouns using Part-of-Speech (POS) tagging. 8. The method of claim 1 wherein extracting the one or more features comprises identifying the one or more features from the cleaned one or more social media inputs using term frequency and inverse document frequency (TF-IDF) weighting. 9. The method of claim 1 wherein the extracted one or more features comprises an n-gram. 10. The method of claim 1 wherein classifying the one or more social media inputs comprises: calculating the probability that the extracted features belong to each predefined category; and selecting a predefined category having the highest probability. 11. The method of claim 1 comprising presenting the classification result to a user via a user interface. 12. The method of claim 11 wherein the presenting the classification result comprises displaying the selected predefined categories and the one or more social media inputs assigned to predefined categories. 13. The method of claim 12 wherein the user interface is configured to allow the user to manually revise the assigned predefined category of the social media input to another predefined category. 14. The method of claim 13 wherein the social media input with a revised category is tagged and re-used in re-training of one or more classifiers. 15. The method of claim 1 comprising training one or more classifiers using a training data set retrieved from a database, wherein each of the social media inputs of the training data set is labeled and assigned to one of the plurality of predefined categories. 16. The method of claim 15 wherein the one or more classifiers comprises Random Forest, Naive Bayes, Logistic Regression and Support Vector Machine (SVM). 17. The method of claim 16 wherein a classifier having the highest accuracy score is selected from the one or more trained classifiers. 18. The method of claim 15 wherein the each of the classifiers comprises a feature space which is divided into different parts, wherein each of the categories occupies a part of the feature space and each part of the feature spaces includes a collection of variables. 19. A system for classifying social media inputs comprising: a non-transitory computer-readable medium for storing a database and computer-readable program code; and one or more processors in communication with the non-transitory computer-readable medium, the one or more processors being operative with the computer-readable program code to perform operations including acquiring one or more social media inputs from one or more social media platforms, cleaning the social media inputs to remove redundant elements, extracting one or more features from the cleaned social media inputs, and classifying, by a trained classifier, the social media inputs into predefined categories using the extracted one or more features. 20. A non-transitory computer-readable medium having stored thereon program code, the program code executable by a computer to perform steps comprising: acquiring one or more social media input from one or more social media platforms; cleaning the one or more social media inputs to remove redundant elements; extracting one or more features from the cleaned one or more social media inputs; and classifying, by a trained classifier, the one or more social media inputs into predefined categories using the extracted one or more features.