System and method for automatic generation of information-rich content from multiple microblogs, each microblog containing only sparse information

1. A method for automatic generation of information-rich content from multiple microblogs, each microblog containing only sparse information, the method comprising:

collecting a population of microblogs comprising microblog data, each microblog containing a limited number of characters;

providing a user interface allowing entry of a search query;

matching a search query entered on the user interface to data in the microblog data, the microblog data based partially on contributor mood;

providing the results of the matching process as a sub-set of microblog data;

applying processing techniques to the sub-set of microblog data; wherein processing techniques include:

using natural language processing techniques to generate at least one category in the sub-set of microblog data, the category based on a link between a sentiment and the sparse information of the sub-set of microblog data;

monitoring microblog contributor mood for the matched search query and observe change over time;

generating contributor mood data for an event in the sub-set of microblog data over time based on a microblog and the category, wherein the contributor mood is associated with the category based on a sentiment in the sparse information of the microblog; and

outputting a group of microblogs based on the sub-set of microblog data associated with the contributor mood data of the event, wherein each microblog is based on a correlation between the matched search query and the sentiment associated with the contributor mood data.

2. The method according to claim 1 , wherein the sentiment is one of a set of sentiments including at least one of positive, negative, funny, sad, happy, associated with a mood category in the microblogs, the method comprising:

dividing the sub-set of microblog data into different categories of microblog based on the sentiment; and

incorporating results for each of the different categories in the summary report.

3. The method according to claim 2 , wherein the category of microblog is selected from at least one of the following:

microblogs containing the matched search query;

microblogs containing sentiment;

microblogs containing positive/negative sentiments, wherein the positive/negative sentiments include a subset of sentiments in each of the positive/negative sentiments;

microblogs containing other forms of sentiment;

microblogs containing sarcastic posts and/or joke-like posts related to the search query;

microblogs containing links related to the search query; and

microblogs containing video links related to the search query.

4. The method according to claim 3 , wherein the natural language processing is selected from at least one of the following:

text normalization

named entity recognition

keyword/key-phrase extraction

sentiment analysis.

5. The method according claim 4 , wherein the processing comprises identifying URLs and expanding URLs.

6. The method according to claim 4 , wherein the summary report provides at least one of the following:

a relationship plot between information need and the extracted items;

navigation of the results over a user-selected time window;

extracted top videos and links related to such events/entity;

extracted top sarcastic posts and jokes-like tweets for the required event; and

extracted top positive/negative and other type of categorized sentiments posts.

7. The method according to claim 4 , wherein the microblog is selected from the group comprising: a post on a social networking site; a comment on a news article; a comment or a post on a forum; a comment on a social networking site; and other user generated content (UGC).

8. The method according to claim 7 , wherein the microblogs are normalised into microblog data having a predetermined format.

9. The method according to claim 8 , wherein normalised microblog data is stored in an index of microblogs to comprise the population of microblogs.

10. The method according to claim 8 , wherein the predetermined format includes at least a selection of the following:

microblog text;

microblog identifier; and

microblog time-stamp.

11. A system for automatic generation of information-rich content from multiple microblogs, each microblog containing only sparse information, the system including:

a computing device having a processor and a memory: and

a storage device;

the computing device being configured to perform the method according to any preceding claim.

12. The system according to claim 11 , further including a visual display for displaying an interface to a user, and to receive a search query from a user, such that the input of a search query by the user causes the computing device to output to the interface a summary report of the processed sub-set of microblog data corresponding to the search query.

13. A computer-readable medium storing instructions which when executed to run on a processor cause the processor to perform the steps according to the method of claim 1 .

14. A system for automatically generating information-rich content from multiple microblogs, each microblog containing only sparse information, the system comprising:

a collection of microblogs comprising microblog data, each microblog containing a limited number of characters, the microblog data based at least partially on contributor mood;

a user interface allowing entry of a search query; and

a hardware processor configured to:

match a search query entered on the user interface to data in the microblog data;

generate at least one category in the matched microblog data, the category based on a link between a sentiment and the sparse information of the matched microblog data;

monitor microblog contributor mood for the matched search query and observe change over time generating contributor mood data for an event in the sub-set of microblog data over time based on a microblog and the category, wherein the contributor mood is associated with the category based on a sentiment in the sparse information of the microblog; and

output a group of microblogs based on the sub-set of microblog data associated with the contributor mood data of the event, wherein each microblog is based on a correlation between the matched search query and the sentiment associated with the contributor mood data.

15. The method according to claim 1 , wherein the contributor mood is based at least partially on emoticons, wherein the sentiment is one of a set of sentiments including at least one of positive, negative, funny, sad, associated with a mood category in the microblogs.