The disclosed embodiments illustrate methods and systems for predicting requirements of a user for resources. The method includes transforming a message posted by the user into a first message vector. The method further includes categorizing one or more first message vectors into one or more categories. The method further includes transforming each of the categorized first message vectors into one or more second message vectors using a wavelet transform technique. The method further includes determining, for each of the categorized first message vectors, a first score based on at least a probability distribution of one or more coefficients associated with each associated feature. The method further includes selecting a predefined number of features based on at least the first score. The method further includes training one or more classifiers on the selected predefined number of features to identify at least the one or more needs of the user.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for predicting one or more requirements of a user for one or more resources over a computer network, said method comprising: transforming, by one or more processors, each message, from one or more messages posted by said user on one or more social media websites, into a first message vector based on at least a comparison with a dictionary, wherein said one or more messages are extracted from one or more social media platform servers; categorizing, by said one or more processors, each of one or more first message vectors into one or more categories based on a predetermined class label associated with respective message; for each categorized first message vector in a category from said one or more categories: transforming, by said one or more processors, said categorized first message vector into one or more second message vectors using a wavelet transform technique, wherein each of said one or more second message vectors has at least one or more associated features, and wherein each of said one or more associated features is represented by one or more coefficients; determining, by said one or more processors, a probability distribution of each of said one or more coefficients; determining, by said one or more processors, a first score, for each associated feature, based on at least said probability distribution of each of said one or more coefficients associated with said each associated feature; and selecting, by said one or more processors, a predefined number of features, from said one or more associated features, based on at least said first score; and training, by said one or more processors, one or more classifiers on said selected predefined number of features, wherein said one or more classifiers predict said one or more requirements of said user for said one or more resources. 2. The method of claim 1, wherein said one or more messages correspond to one or more of one or more text messages, one or more images, one or more audios, and one or more videos. 3. The method of claim 1, wherein said one or more resources correspond to one or more of one or more products and one or more services associated with one or more domains, and wherein said one or more domains include one or more of a health domain, a finance domain, an education domain, and a job domain. 4. The method of claim 1 further comprising pre-processing, by said one or more processors, said one or more messages using one or more natural language processing techniques, wherein said pre-processing comprises at least one of a tokenization, a stemming, or a normalization of said one or more messages. 5. The method of claim 1 further comprising eliminating, by said one or more processors, a noisy data from said one or more messages by utilizing a relevance detection technique. 6. The method of claim 1 further comprising determining, by said one or more processors, a second score for a first associated feature based on said first score of said first associated feature in said category and said first score of a second associated feature in remaining one or more categories. 7. The method of claim 6 further comprising ranking, by said one or more processors, said one or more associated features corresponding to each of said one or more second message vectors based on at least said second score. 8. The method of claim 7, wherein said predefined number of features is selected based on said ranking. 9. The method of claim 1, wherein said one or more classifiers are trained based on one or more machine learning techniques comprising at least one of a Support Vector Machine (SVM), a Logistic Regression, a Bayesian Classifier, a Decision Tree Classifier, or a Copula-based Classifier, a K-Nearest Neighbors (KNN) Classifier, or a Random Forest (RF) Classifier. 10. A system for predicting one or more requirements of a user for one or more resources over a computer network, said system comprising: one or more processors configured to: transform each message, from one or more messages posted by said user on one or more social media websites, into a first message vector based on at least a comparison with a dictionary, wherein said one or more messages are extracted from one or more social media platform servers; categorize each of one or more first message vectors into one or more categories based on a predetermined class label associated with respective message; for each categorized first message vector in a category from said one or more categories: transform said categorized first message vector into one or more second message vectors using a wavelet transform technique, wherein each of said one or more second message vectors has at least one or more associated features, and wherein each of said one or more associated features is represented by one or more coefficients; determine a probability distribution of each of said one or more coefficients; determine a first score, for each associated feature, based on at least said probability distribution of each of said one or more coefficients associated with said each associated feature; and select a predefined number of features, from said one or more associated features, based on at least said first score; and train one or more classifiers on said selected predefined number of features, wherein said one or more classifiers predict said one or more requirements of said user for said one or more resources. 11. The system of claim 10, wherein said one or more messages correspond to one or more of one or more text messages, one or more images, one or more audios, and one or more videos. 12. The system of claim 10, wherein said one or more processors are further configured to pre-process said one or more messages using one or more natural language processing techniques, wherein said pre-processing comprises at least one of a tokenization, a stemming, or a normalization of said one or more messages. 13. The system of claim 10, wherein said one or more processors are further configured to eliminate a noisy data from said one or more messages by utilizing a relevance detection technique. 14. The method of claim 10, wherein said one or more processors are further configured to determine a second score for a first associated feature based on said first score of said first associated feature in said category and said first score of a second associated feature in remaining one or more categories. 15. The system of claim 14, wherein said one or more processors are further configured to rank said one or more associated features corresponding to each of said one or more second message vectors based on at least said second score. 16. The system of claim 15, wherein said predefined number of features is selected based on said ranking. 17. The system of claim 10, wherein said one or more classifiers are trained based on one or more machine learning techniques comprising at least one of a Support Vector Machine (SVM), a Logistic Regression, a Bayesian Classifier, a Decision Tree Classifier, or a Copula-based Classifier, a K-Nearest Neighbors (KNN) Classifier, or a Random Forest (RF) Classifier. 18. The system of claim 1, wherein said one or more processors are further configured to transmit a recommendation of said predicted said one or more requirements of said user for said one or more resources over said computer network. 19. A computer program product for use with a computer, the computer program product comprising a non-transitory computer readable medium, wherein the non-transitory computer readable medium stores a computer program code for predicting one or more requirements of a user for one or more resources over a computer network, wherein the computer program code is executable by one or more processors to: transform each message, from one or more messages posted by said user on one or more social media websites, into a first message vector based on at least a comparison with a dictionary, wherein said one or more messages are extracted from one or more social media platform servers; categorize each of one or more first message vectors into one or more categories based on a predetermined class label associated with respective message; for each categorized first message vector in a category from said one or more categories: transform said categorized first message vector into one or more second message vectors using a wavelet transform technique, wherein each of said one or more second message vectors has at least one or more associated features, and wherein each of said one or more associated features is represented by one or more coefficients; determine a probability distribution of each of said one or more coefficients; determine a first score, for each associated, feature based on at least said probability distribution of each of said one or more coefficients associated with said each associated feature; and select a predefined number of features, from said one or more associated features, based on at least said first score; and train one or more classifiers on said selected predefined number of features, wherein said one or more classifiers predict said one or more requirements of said user for said one or more resources.