intent detection, text mining, support vector machines, N-grams, parts of speech
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.
Faculty of Applied Science and Technology (FAST)
Future Technologies Conference (FTC) 2017
This research was supported by NSERC through the Engage grant.
Creative Commons 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.
Akulick, Samantha and Mahmoud, El Sayed, "Intent Detection through Text Mining and Analysis" (2017). Publications and Scholarship. 4.
Artificial Intelligence and Robotics Commons, Computer Engineering Commons, Numerical Analysis and Scientific Computing Commons, Theory and Algorithms Commons
This paper was presented at the Future Technologies Conference (FTC) 2017 on November 29, 2017, in Vancouver, Canada.