A method for inferring a location where each textual message was posted by a test user. The method includes partitioning an area into plural sub-areas, associating each of textual messages posted by training users with a sub-area where each of the textual message was posted, extracting a keyword from the textual message associated with each sub-area, constructing a feature vector of each sub-area, based on the extracted keyword, computing a transition probability for each sub-area by time-series of location information associated with the textual messages posted by the training user, the transition probability being a probability of transition from a target sub-area to other sub-areas, computing plural scores of each location, using the feature vector, where each textual message was posted by the test user, and computing, based on the plural scores and the transition probability, time-series of locations where each textual message was posted by the test user.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method for inferring a location where each textual message was posted by a test user, the method comprising: partitioning an area into a plurality of sub-areas; associating textual messages posted by training users with a sub-area where each textual message was posted, wherein each device location of the training users is open to public; extracting a keyword from each textual message posted by the training users associated with each sub-area; constructing a feature vector of each sub-area, based on the extracted keyword; computing a transition probability for each sub-area by a time-series of location information associated with the textual messages posted by the training user, wherein the transition probability is a probability of transition from a target sub-area to other sub-areas; computing a plurality of scores of each location, using the feature vector, where each textual message was posted by the test user; and computing, based on the plurality of scores and the transition probability, time-series of locations where each textual message was posted by the test user. 2. The method according to claim 1, wherein computing the transition probability comprises computing, using training data sets obtained from the training users, the probability of transition from the target sub-area to the other sub-areas which is distant by d from the target sub-area after a predefined time. 3. The method according to claim 1, wherein computing the transition probability comprises computing the transition probability for each sub-area, based on an average velocity of velocities calculated from training data sets obtained from the training users. 4. The method according to claim 1, wherein computing the transition probability comprises computing the transition probability for each sub-area, based on frequencies of transition from the target sub-area to the other sub-areas, the frequencies being calculated from training data sets obtained from the training users. 5. The method according to claim 1, wherein computing the plurality of scores is performed with a statistical classifier. 6. The method according to claim 1, wherein computing the time-series of locations are performed with a dynamic programming algorithm. 7. The method according to claim 1, wherein partitioning the area into the plurality of sub-areas comprises partitioning the area into a plurality of sub-areas having the same or different area sizes. 8. The method according to claim 1, wherein constructing the feature vector for each sub-area, based on the extracted keyword, comprises constructing the feature vector for each sub-area, based on the existence of each keyword, frequency of each keyword, importance of each keyword, or a combination thereof. 9. The method according to claim 1, wherein constructing the feature vector for each sub-area, based on the extracted keyword, comprises smoothing the feature vector for each sub-area. 10. The method according to claim 1, wherein the test user is dynamically moving with a device having a function to post the textual message. 11. The method according to claim 1, further comprising: displaying the time series of the locations or location where each textual message was posted by the test user. 12. A system for inferring a location where each textual message was posted by a test user, comprising: a processor; and a memory storing a program, which, when executed on the processor, performs a method for inferring a location where each textual message was posted by a test user, the method comprising: partitioning an area into a plurality of sub-areas; associating textual messages posted by training users with a sub-area where each textual message was posted, wherein each device location of the training user is open to public; extracting a keyword from each textual message posted by the training users associated with each sub-area; constructing a feature vector of each sub-area, based on the extracted keyword; computing a transition probability for each sub-area by time-series of location information associated with the textual messages posted by the training user, wherein the transition probability is a probability of transition from a target sub-area to other sub-areas; computing a plurality of scores of each location, using the feature vector, where each textual message was posted by the test user; and computing, based on the plurality of scores and the transition probability, time-series of locations where each textual message was posted by the test user. 13. The system according to claim 12, wherein the computing the transition probability comprises computing, using training data sets obtained from the training users, the probability of transition from the target sub-area to the other sub-areas which is distant by d from the target sub-area after a predefined time. 14. The system according to claim 12, wherein computing the transition probability comprises computing the transition probability for each sub-area, based on an average velocity of velocities calculated from training data sets obtained from the training users. 15. The system according to claim 12, wherein computing the transition probability comprises computing the transition probability for each sub-area, based on frequencies of transition from the target sub-area to the other sub-areas, the frequencies being calculated from training data sets obtained from the training users. 16. The system according to claim 12, the method further comprising displaying the time series of the locations or location where each textual message was posted by the test user. 17. A computer program product for inferring a location where each textual message was posted by a test user, the computer program product comprising a non-transitory computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to perform a method comprising: partitioning an area into a plurality sub-areas; associating textual messages posted by a training user with a sub-area where each textual message was posted, wherein each device location of the training users is open to public; extracting a keyword from each textual message posted by the training users associated with each sub-area; constructing a feature vector of each sub-area, based on the extracted keyword; computing a transition probability for each sub-area by time-series of location information associated with the textual messages posted by the training user, wherein the transition probability is a probability of transition from a target sub-area to other sub-areas; computing a plurality of scores of each location, using the feature vector, where each textual message was posted by the test user; and computing, based on the plurality of scores and the transition probability, time-series of locations where each textual message was posted by the test user. 18. The computer program product according to claim 17, wherein computing the transition probability comprises computing, using training data sets obtained from the training users, the probability of transition from the target sub-area to the other sub-areas which is distant by d from the target sub-area after a predefined time. 19. The computer program product according to claim 17, wherein computing the transition probability comprises computing the transition probability for each sub-area, based on an average velocity of velocities calculated from training data sets obtained from the training users. 20. The computer program product according to claim 17, wherein computing the transition probability comprises computing the transition probability for each sub-area, based on frequencies of transition from the target sub-area to the other sub-areas, the frequencies being calculated from training data sets obtained from the training users.