Tracking Heteroscedastic Progression of Opinions with an Aggregation Bias Correction Model

Ahsan Ijaz, Jongeun Choi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this work, we propose a temporal aggregation scheme for sentiments expressed in social networks. The proposed method discounts for the bias caused in aggregation due to classification errors while providing confidence intervals. A computationally efficient prediction and interpolation scheme of temporal progression is discussed that accounts for the heteroscedastic nature of noise. To this end, we use a heteroscedastic gaussian process model. To test the efficacy of our proposed method, we use tweets about Donald Trump obtained for a period of twelve hours. The results are generalized using six state of art classification schemes for predicting sentiments. Our method shows improvement in R2 statistics with better coverage under proposed uncertainty for all the six classification schemes. Finally, the results of variational heteroscedastic gaussian process (VHGP) regression are discussed and the normalized mean square error with negative log-probabilty density of the prediction are reported. It is further shown that the volatility of opinion tracking in social network data streams is better captured with a heteroscedastic noise model.

Original languageEnglish
Title of host publicationProceedings - 2016 IEEE 10th International Conference on Semantic Computing, ICSC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages346-349
Number of pages4
ISBN (Electronic)9781509006618
DOIs
Publication statusPublished - 2016 Mar 22
Event10th IEEE International Conference on Semantic Computing, ICSC 2016 - Laguna Hills, United States
Duration: 2016 Feb 32016 Feb 5

Other

Other10th IEEE International Conference on Semantic Computing, ICSC 2016
CountryUnited States
CityLaguna Hills
Period16/2/316/2/5

Fingerprint

Agglomeration
Mean square error
Interpolation
Statistics
Uncertainty

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Information Systems

Cite this

Ijaz, A., & Choi, J. (2016). Tracking Heteroscedastic Progression of Opinions with an Aggregation Bias Correction Model. In Proceedings - 2016 IEEE 10th International Conference on Semantic Computing, ICSC 2016 (pp. 346-349). [7439361] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICSC.2016.100
Ijaz, Ahsan ; Choi, Jongeun. / Tracking Heteroscedastic Progression of Opinions with an Aggregation Bias Correction Model. Proceedings - 2016 IEEE 10th International Conference on Semantic Computing, ICSC 2016. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 346-349
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Ijaz, A & Choi, J 2016, Tracking Heteroscedastic Progression of Opinions with an Aggregation Bias Correction Model. in Proceedings - 2016 IEEE 10th International Conference on Semantic Computing, ICSC 2016., 7439361, Institute of Electrical and Electronics Engineers Inc., pp. 346-349, 10th IEEE International Conference on Semantic Computing, ICSC 2016, Laguna Hills, United States, 16/2/3. https://doi.org/10.1109/ICSC.2016.100

Tracking Heteroscedastic Progression of Opinions with an Aggregation Bias Correction Model. / Ijaz, Ahsan; Choi, Jongeun.

Proceedings - 2016 IEEE 10th International Conference on Semantic Computing, ICSC 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 346-349 7439361.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Ijaz A, Choi J. Tracking Heteroscedastic Progression of Opinions with an Aggregation Bias Correction Model. In Proceedings - 2016 IEEE 10th International Conference on Semantic Computing, ICSC 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 346-349. 7439361 https://doi.org/10.1109/ICSC.2016.100