Unifying Topic, Sentiment & Preference in an HDP-Based Rating Regression Model for Online Reviews

Zheng Chen, Yong Zhang, Yue Shang, Xiaohua Hu
; Proceedings of The 8th Asian Conference on Machine Learning, PMLR 63:222-237, 2016.

Abstract

This paper proposes a new HDP based online review rating regression model named Topic-Sentiment-Preference Regression Analysis (TSPRA). TSPRA combines topics (i.e. product as-pects), word sentiment and user preference as regression factors, and is able to perform topic clus-tering, review rating prediction, sentiment analysis and what we invent as “critical aspect” analysis altogether in one framework. TSPRA extends sentiment approaches by integrating the key concept “user preference” in collaborative filtering (CF) models into consideration, while it is distinct from current CF models by decoupling “user preference” and “sentiment” as independent factors. Our experiments conducted on 22 Amazon datasets show overwhelming better performance in rating predication against a state-of-art model FLAME (2015) in terms of error, Pearson’s Correlation and number of inverted pairs. For sentiment analysis, we compare the derived word sentiments against a public sentiment resource SenticNet3 and our sentiment estimations clearly make more sense in the context of online reviews. Last, as a result of the de-correlation of “user preference” from “sentiment”, TSPRA is able to evaluate a new concept “critical aspects”, defined as the prod-uct aspects seriously concerned by users but negatively commented in reviews. Improvement to such “critical aspects” could be most effective to enhance user experience.

Cite this Paper


BibTeX
@InProceedings{pmlr-v63-chen33, title = {Unifying Topic, Sentiment \& Preference in an HDP-Based Rating Regression Model for Online Reviews}, author = {Zheng Chen and Yong Zhang and Yue Shang and Xiaohua Hu}, booktitle = {Proceedings of The 8th Asian Conference on Machine Learning}, pages = {222--237}, year = {2016}, editor = {Robert J. Durrant and Kee-Eung Kim}, volume = {63}, series = {Proceedings of Machine Learning Research}, address = {The University of Waikato, Hamilton, New Zealand}, month = {16--18 Nov}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v63/chen33.pdf}, url = {http://proceedings.mlr.press/v63/chen33.html}, abstract = {This paper proposes a new HDP based online review rating regression model named Topic-Sentiment-Preference Regression Analysis (TSPRA). TSPRA combines topics (i.e. product as-pects), word sentiment and user preference as regression factors, and is able to perform topic clus-tering, review rating prediction, sentiment analysis and what we invent as “critical aspect” analysis altogether in one framework. TSPRA extends sentiment approaches by integrating the key concept “user preference” in collaborative filtering (CF) models into consideration, while it is distinct from current CF models by decoupling “user preference” and “sentiment” as independent factors. Our experiments conducted on 22 Amazon datasets show overwhelming better performance in rating predication against a state-of-art model FLAME (2015) in terms of error, Pearson’s Correlation and number of inverted pairs. For sentiment analysis, we compare the derived word sentiments against a public sentiment resource SenticNet3 and our sentiment estimations clearly make more sense in the context of online reviews. Last, as a result of the de-correlation of “user preference” from “sentiment”, TSPRA is able to evaluate a new concept “critical aspects”, defined as the prod-uct aspects seriously concerned by users but negatively commented in reviews. Improvement to such “critical aspects” could be most effective to enhance user experience.} }
Endnote
%0 Conference Paper %T Unifying Topic, Sentiment & Preference in an HDP-Based Rating Regression Model for Online Reviews %A Zheng Chen %A Yong Zhang %A Yue Shang %A Xiaohua Hu %B Proceedings of The 8th Asian Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2016 %E Robert J. Durrant %E Kee-Eung Kim %F pmlr-v63-chen33 %I PMLR %J Proceedings of Machine Learning Research %P 222--237 %U http://proceedings.mlr.press %V 63 %W PMLR %X This paper proposes a new HDP based online review rating regression model named Topic-Sentiment-Preference Regression Analysis (TSPRA). TSPRA combines topics (i.e. product as-pects), word sentiment and user preference as regression factors, and is able to perform topic clus-tering, review rating prediction, sentiment analysis and what we invent as “critical aspect” analysis altogether in one framework. TSPRA extends sentiment approaches by integrating the key concept “user preference” in collaborative filtering (CF) models into consideration, while it is distinct from current CF models by decoupling “user preference” and “sentiment” as independent factors. Our experiments conducted on 22 Amazon datasets show overwhelming better performance in rating predication against a state-of-art model FLAME (2015) in terms of error, Pearson’s Correlation and number of inverted pairs. For sentiment analysis, we compare the derived word sentiments against a public sentiment resource SenticNet3 and our sentiment estimations clearly make more sense in the context of online reviews. Last, as a result of the de-correlation of “user preference” from “sentiment”, TSPRA is able to evaluate a new concept “critical aspects”, defined as the prod-uct aspects seriously concerned by users but negatively commented in reviews. Improvement to such “critical aspects” could be most effective to enhance user experience.
RIS
TY - CPAPER TI - Unifying Topic, Sentiment & Preference in an HDP-Based Rating Regression Model for Online Reviews AU - Zheng Chen AU - Yong Zhang AU - Yue Shang AU - Xiaohua Hu BT - Proceedings of The 8th Asian Conference on Machine Learning PY - 2016/11/20 DA - 2016/11/20 ED - Robert J. Durrant ED - Kee-Eung Kim ID - pmlr-v63-chen33 PB - PMLR SP - 222 DP - PMLR EP - 237 L1 - http://proceedings.mlr.press/v63/chen33.pdf UR - http://proceedings.mlr.press/v63/chen33.html AB - This paper proposes a new HDP based online review rating regression model named Topic-Sentiment-Preference Regression Analysis (TSPRA). TSPRA combines topics (i.e. product as-pects), word sentiment and user preference as regression factors, and is able to perform topic clus-tering, review rating prediction, sentiment analysis and what we invent as “critical aspect” analysis altogether in one framework. TSPRA extends sentiment approaches by integrating the key concept “user preference” in collaborative filtering (CF) models into consideration, while it is distinct from current CF models by decoupling “user preference” and “sentiment” as independent factors. Our experiments conducted on 22 Amazon datasets show overwhelming better performance in rating predication against a state-of-art model FLAME (2015) in terms of error, Pearson’s Correlation and number of inverted pairs. For sentiment analysis, we compare the derived word sentiments against a public sentiment resource SenticNet3 and our sentiment estimations clearly make more sense in the context of online reviews. Last, as a result of the de-correlation of “user preference” from “sentiment”, TSPRA is able to evaluate a new concept “critical aspects”, defined as the prod-uct aspects seriously concerned by users but negatively commented in reviews. Improvement to such “critical aspects” could be most effective to enhance user experience. ER -
APA
Chen, Z., Zhang, Y., Shang, Y. & Hu, X.. (2016). Unifying Topic, Sentiment & Preference in an HDP-Based Rating Regression Model for Online Reviews. Proceedings of The 8th Asian Conference on Machine Learning, in PMLR 63:222-237

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