Extracting Phrases Describing Problems with Products and Services from Twitter Messages

Authors

  • Narendra K. Gupta AT&T Labs - Research

DOI:

https://doi.org/10.13053/cys-17-2-1524

Keywords:

Social media, information extraction, text classification.

Abstract

Social media contain many types ofinformation useful to businesses. In this paper wediscuss a trigger-target based approach to extractdescriptions of problems from Twitter data. It is importantto note that the descriptions of problems are factualstatements as opposed to subjective opinions aboutproducts/services. We first identify the problem tweetsi.e. the tweets containing descriptions of problems.We then extract the phrases that describe the problem.In our approach such descriptions are extracted as acombination of trigger and target phrases. Triggersare mostly domain independent verb phrases and areidentified by using hand crafted lexical and syntacticpatterns. Targets on the other hand are domain specificnoun phrases syntactically related to the triggers. Weframe the problem of finding target phrase correspondingto a trigger phrase as a ranking problem and show theresults of experiments with maximum entropy classifiersand voted perceptrons. Both approaches outperform therule based approach reported before.

Author Biography

Narendra K. Gupta, AT&T Labs - Research

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Published

2013-06-29

Issue

Vol. 17 No. 2 (2013): 17(2) April-June 2013

Section

Articles

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