Name
Abstract | ||
|---|---|---|
Diverse social networks and online communities add more messages to catch up to active communication users who are already flooded by emails, SMS, and IMs. We present application frameworks and methods that aim to assist the users to efficiently identify important messages from diverse interactive message channels, in particular, as an example, the messages containing actionable items. To build the method for identifying the messages containing actionable items, an empirical study on choosing the machine learning based classification algorithm and construction of the best feature set was conducted, illustrating the various aspects to consider for the best classification performance. A set of novel and elegant rules for parsing and extracting a summary of the actionable items from the identified message are presented and its effectiveness is demonstrated through examples and measurements on an email data set. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1145/2184751.2184758 | ICUIMC |
Keywords | Field | DocType |
email data,active communication user,diverse interactive message channel,important message,classification algorithm,best classification performance,application framework,diverse social network,elegant rule,best feature set,actionable item,information extraction,classification,machine learning,natural language processing,social network,empirical study | World Wide Web,Social network,Computer science,Communication channel,Feature set,Information extraction,Parsing,Empirical research | Conference |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eunsoo Shim | 1 | 30 | 4.66 |
| Varun Singla | 2 | 1 | 0.70 |
| Venkatesh Krishnaswamy | 3 | 40 | 10.26 |