Embodiments relate to user attitude modeling and behavior prediction for a social media network. One aspect includes collecting data relating to previously demonstrated sentiments, opinions, and actions attributed to network users toward a topic. Another aspect includes creating a model from the data, which includes factorizing the actions for behavior inference, factorizing auxiliary content from the network for opinion and sentiment inferences, and applying sentiment and opinion regularization to constrain user preferences on implicit topics to explicit sentiments and explicit opinions. Another aspect includes applying the model to a new user of the network with respect to the topic, and generating a prediction with respect to the user that includes predicting sentiment and opinion as a function of the auxiliary content and feature coefficients learned during a training process, and predicting a future action of the user as a function of the auxiliary content and latent profiles of the topic.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for user attitude modeling and behavior prediction for a social media network, the method comprising: collecting data relating to previously demonstrated sentiments, opinions, and actions attributed to a plurality of social media network users toward a topic; creating a model from the data, comprising: factorizing the actions for behavior inference; factorizing auxiliary content from the social media network for opinion and sentiment inferences; and applying sentiment regularization and opinion regularization for respective sentiments and opinions to constrain user preferences attributable to the plurality of social media network users on implicit topics to explicit sentiments and explicit opinions; applying the model to a new user of the social media network with respect to the topic; and generating a prediction with respect to the new user comprising predicting sentiment and opinion of the new user as a function of the auxiliary content and feature coefficients learned during a training process, and predicting a future action of the new user as a function of the auxiliary content and latent profiles of the topic. 2. The method of claim 1, further comprising: optimizing model parameters of the model using alternating non-negative update rules. 3. The method of claim 1, further comprising: determining an overall sentiment for each of the social media network users from multiple opinions held with respect to the topic, the overall sentiment predicted by incorporating a transition matrix into the model to capture relationships between the opinions and corresponding sentiments. 4. The method of claim 1, wherein the auxiliary content includes at least one of: user profile data; content of social media network posts; and aspects of the social media network. 5. The method of claim 1, wherein the collecting data relating to previously demonstrated sentiments, opinions, and actions includes parameterizing a user-configurable number of postings for use by the model. 6. The method of claim 1, wherein the topic is one of multiple topics, and the collecting data relating to previously demonstrated sentiments, opinions, and actions includes parameterizing a number of the multiple topics for the collecting. 7. The method of claim 1, wherein the collecting data relating to previously demonstrated sentiments, opinions, and actions includes parameterizing at least one of the plurality of social media network users' postings into a user-configurable number of posts in temporal dimension. 8. A computer program product for user attitude modeling and behavior prediction for a social media network, the computer program product comprising a computer readable storage medium having program code embodied therewith, the program code executable by a processor to: collect data relating to previously demonstrated sentiments, opinions, and actions attributed to a plurality of social media network users toward a topic; create a model from the data, comprising: factorize the actions for behavior inference; factorize auxiliary content from the social media network for opinion and sentiment inferences; and apply sentiment regularization and opinion regularization for respective sentiments and opinions to constrain user preferences attributable to the plurality of social media network users on implicit topics to explicit sentiments and explicit opinions; apply the model to a new user of the social media network with respect to the topic; and generate a prediction with respect to the new user comprising predicting sentiment and opinion of the new user as a function of the auxiliary content and feature coefficients learned during a training process, and predicting a future action of the new user as a function of the auxiliary content and latent profiles of the topic. 9. The computer program product of claim 8, wherein the program code is executable by a processor to: optimize model parameters of the model using alternating non-negative update rules. 10. The computer program product of claim 8, wherein the program code is executable by a processor to: determine an overall sentiment for each of the social media network users from multiple opinions held with respect to the topic, the overall sentiment predicted by incorporating a transition matrix into the model to capture relationships between the opinions and corresponding sentiments. 11. The computer program product of claim 8, wherein the auxiliary content includes at least one of: user profile data; content of social media network posts; and aspects of the social media network. 12. The computer program product of claim 8, wherein collection of the data relating to previously demonstrated sentiments, opinions, and actions includes parameterizing a user-configurable number of postings for use by the model. 13. The computer program product of claim 8, wherein the topic is one of multiple topics, and collection of the data relating to previously demonstrated sentiments, opinions, and actions includes parameterizing a number of the multiple topics for the collection. 14. The computer program product of claim 8, wherein collection of the data relating to previously demonstrated sentiments, opinions, and actions includes parameterizing at least one of the plurality of social media network users' postings into a user-configurable number of posts in temporal dimension. 15. A system for user attitude modeling and behavior prediction for a social media network, the system comprising: a memory having computer readable computer instructions; and a processor for executing the computer readable instructions, the computer readable instructions including: collecting data relating to previously demonstrated sentiments, opinions, and actions attributed to a plurality of social media network users toward a topic; creating a model from the data, comprising: factorizing the actions for behavior inference; factorizing auxiliary content from the social media network for opinion and sentiment inferences; and applying sentiment regularization and opinion regularization for respective sentiments and opinions to constrain user preferences attributable to the plurality of social media network users on implicit topics to explicit sentiments and explicit opinions; applying the model to a new user of the social media network with respect to the topic; and generating a prediction with respect to the new user comprising predicting sentiment and opinion of the new user as a function of the auxiliary content and feature coefficients learned during a training process, and predicting a future action of the new user as a function of the auxiliary content and latent profiles of the topic. 16. The system of claim 15, wherein the computer readable instructions include: determining an overall sentiment for each of the social media network users from multiple opinions held with respect to the topic, the overall sentiment predicted by incorporating a transition matrix into the model to capture relationships between the opinions and corresponding sentiments. 17. The system of claim 15, wherein the auxiliary content includes at least one of: user profile data; content of social media network posts; and aspects of the social media network. 18. The system of claim 15, wherein the collecting data relating to previously demonstrated sentiments, opinions, and actions includes parameterizing a user-configurable number of postings for use by the model. 19. The system of claim 15, wherein the topic is one of multiple topics, and the collecting data relating to previously demonstrated sentiments, opinions, and actions includes parameterizing a number of the multiple topics for the collecting. 20. The system of claim 15, wherein the collecting data relating to previously demonstrated sentiments, opinions, and actions includes parameterizing at least one of the plurality of social media network users' postings into a user-configurable number of posts in temporal dimension.