Predicting the business impact of tweet conversations

Patent metadata

Field	Value
Publication number	US-2016019565-A1
Application number	US-201514729170-A
Country	US
Kind code	A1
Filing date	Jun 3, 2015
Priority date	Jul 15, 2014
Publication date	Jan 21, 2016
Grant date	—

Abstract

A system and methods are provided for identifying conversations in tweet streams. A method includes grouping tweet messages in the tweet streams into tweet groups, responsive to hashtags therefor and time intervals in which the tweet message were sent. The method further includes splitting the tweet groups into subgroups responsive to secondary hashtags and a time separation between the tweets messages. The method also includes clustering any of the subgroups into a respective same conversation responsive to word occurrences, word frequencies, and account holders. The method additionally includes merging any of the subgroups having different hashtags into the respective same conversation responsive to overlapping glossary and account lists. Each of the tweet groups and each of the subgroups correspond to a respective different one of the conversations when unable to be split, clustered, or merged.

First claim

1 - 3 . (canceled)

4 . A computer program product for identifying conversations in tweet streams, the computer program product comprising a 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:
grouping tweet messages in the tweet streams into tweet groups, responsive to hashtags therefor and time intervals in which the tweet message were sent; splitting the tweet groups into subgroups responsive to secondary hashtags and a time separation between the tweets messages; clustering any of the subgroups into a respective same conversation responsive to word occurrences, word frequencies, and account holders; and merging any of the subgroups having different hashtags into the respective same conversation responsive to overlapping glossary and account lists, wherein each of the tweet groups and each of the subgroups correspond to a respective different one of the conversations when unable to be split, clustered, or merged.

5 - 17 . (canceled)

18 . A computer program product for predicting the business impact of input tweet conversations, the computer program product comprising a 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:
creating training data that includes pre-selected tweet conversations, pre-selected hashtags from the pre-selected tweet conversations, and labels, each of the labels specifying a respective predicted business impact level for a respective one of the pre-selected tweet conversations and a respective one of the pre-selected hashtags included therein; computing, by a processor, feature vectors for features extracted from the input tweet conversations; and forming a prediction model, trained by the training data, for predicting a respective business impact level for each of the input tweet conversations, by mapping respective predicted business impact levels to one or more feature vectors of each of the input tweet conversations.

19 . A system for predicting the business impact of input tweet conversations, comprising:
a database for storing training data that includes pre-selected tweet conversations, pre-selected hashtags from the pre-selected tweet conversations, and labels, each of the labels specifying a respective predicted business impact level for a respective one of the pre-selected tweet conversations and a respective one of the pre-selected hashtags included therein; a feature vector computer, having a processor, for computing feature vectors for features extracted from the input tweet conversations; and an impact predictor, having a prediction model trained by the training data, for predicting a respective business impact level for each of the input tweet conversations, by mapping respective predicted business impact levels to one or more feature vectors of each of the input tweet conversations.

20 . The system of claim 19 , wherein the business impact level is predicted using a binary specifier, the binary specified being selected from a value of high and a value of low.

Assignees

• IBM

Inventors

• Doganata Yurdaer N
• Lin Ching-Yung
• Luna David C
• Mestre Jordi C
• Pages Xavier N
• Topkara Mercan
• Wen Zhen
• Yeh Danny L

Classifications

• G06Q10/40

  Business processes related to social networking or social networking services · CPC title

• H04L12/185

  with management of multicast group membership · CPC title

• G06Q30/0202 Primary

  Market predictions or forecasting for commercial activities · CPC title

• G06Q50/01

  Physics · mapped topic

• H04L51/16

  Electricity · mapped topic