TY - JOUR

T1 - Extracting Prominent Aspects of Online Customer Reviews: A Data-Driven Approach to Big Data Analytics

AU - Ali, Noaman M.

AU - Alshahrani, Abdullah

AU - Alghamdi, Ahmed M.

AU - Novikov, Boris

N1 - Funding Information: Acknowledgments: The researcher [Noaman M. Ali] is funded by a scholarship [EGY-6428/17] under the Joint Executive Program between the Arab Republic of Egypt and the Russian Federation. Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

PY - 2022/6/29

Y1 - 2022/6/29

N2 - Sentiment analysis on social media and e-markets has become an emerging trend. Extracting aspect terms for structure-free text is the primary task incorporated in the aspect-based sentiment analysis. This significance relies on the dependency of other tasks on the results it provides, which directly influences the accuracy of the final results of the sentiment analysis. In this work, we propose an aspect term extraction model to identify the prominent aspects. The model is based on clustering the word vectors generated using the pre-trained word embedding model. Dimensionality reduction was employed to improve the quality of word clusters obtained using the K-Means++ clustering algorithm. The proposed model was tested on the real datasets collected from online retailers’ websites and the SemEval-14 dataset. Results show that our model outperforms the baseline models.

AB - Sentiment analysis on social media and e-markets has become an emerging trend. Extracting aspect terms for structure-free text is the primary task incorporated in the aspect-based sentiment analysis. This significance relies on the dependency of other tasks on the results it provides, which directly influences the accuracy of the final results of the sentiment analysis. In this work, we propose an aspect term extraction model to identify the prominent aspects. The model is based on clustering the word vectors generated using the pre-trained word embedding model. Dimensionality reduction was employed to improve the quality of word clusters obtained using the K-Means++ clustering algorithm. The proposed model was tested on the real datasets collected from online retailers’ websites and the SemEval-14 dataset. Results show that our model outperforms the baseline models.

KW - aspect-based sentiment analysis

KW - aspect-term extraction

KW - feature extraction

KW - natural language processing

KW - word embedding

UR - http://www.scopus.com/inward/record.url?scp=85133017936&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/3d8a3ea1-fed4-3acf-bc8c-9764a8ec3e30/

U2 - 10.3390/electronics11132042

DO - 10.3390/electronics11132042

M3 - Article

AN - SCOPUS:85133017936

VL - 11

JO - Electronics (Switzerland)

JF - Electronics (Switzerland)

SN - 2079-9292

IS - 13

M1 - 2042

ER -