Document Type

Conference Presentation

Publication Date

11-2017

Keywords

intent detection, text mining, support vector machines, N-grams, parts of speech

Abstract

The article is about the work investigated using n-grams, parts-Of-Speech and Support Vector machines for detecting the customer intents in the user generated contents. The work demonstrated a system of categorization of customer intents that is concise and useful for business purposes. We examined possible sources of text posts to be analyzed using three text mining algorithms. We presented the three algorithms and the results of testing them in detecting different six intents. This work established that intent detection can be performed on text posts with approximately 61% accuracy.

Comments

This paper was presented at the Future Technologies Conference (FTC) 2017 on November 29, 2017, in Vancouver, Canada.

Faculty

Faculty of Applied Science and Technology (FAST)

Journal

Future Technologies Conference (FTC) 2017

Version

Pre-print

Funder

This research was supported by NSERC through the Engage grant.

Terms of Use

Terms of Use for Works posted in SOURCE.

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Original Publication Citation

Mahmoud, E. S. (2017). Intent detection through text mining and analysis. Conference paper from Future Technologies Conference (FTC) 2017. Vancouver, Canada: SAI.

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