Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
A Multi-Source Big Data Framework for Capturing and Analyzing Customer Feedback. / Ali, Noaman M.; Novikov, Boris.
Proceedings of the 2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, ElConRus 2021. ed. / S. Shaposhnikov. Institute of Electrical and Electronics Engineers Inc., 2021. p. 185-190 9396606 (IEEE NW Russia Young Researchers in Electrical and Electronic Engineering Conference).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
}
TY - GEN
T1 - A Multi-Source Big Data Framework for Capturing and Analyzing Customer Feedback
AU - Ali, Noaman M.
AU - Novikov, Boris
N1 - Publisher Copyright: © 2021 IEEE. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021/4/9
Y1 - 2021/4/9
N2 - Big Data refers to the highly growing digital data collections that involve data with different formats, including structured, semi-structured, and unstructured datasets. Analyzing these combinations requires capabilities beyond the traditional database management systems' abilities. Among most sources of big data appears e-markets and social media platforms as significant contributors. This distinction is due to its features that facilitate consumers to express their views or opinions about specific products and services. Customer reviews and ratings become a significant resource for both consumers and firms regarding their plentiful and valuable knowledge. The proposed work introduces a big data framework to analyze such reviews and ratings, starting with data collection from different sources. Followed by integrating the collected data, which comes in different formats, toward the further processing phase. Finally, the analysis and visualization steps to draw the conclusions. Our work was tested on real data collected from active web resources.
AB - Big Data refers to the highly growing digital data collections that involve data with different formats, including structured, semi-structured, and unstructured datasets. Analyzing these combinations requires capabilities beyond the traditional database management systems' abilities. Among most sources of big data appears e-markets and social media platforms as significant contributors. This distinction is due to its features that facilitate consumers to express their views or opinions about specific products and services. Customer reviews and ratings become a significant resource for both consumers and firms regarding their plentiful and valuable knowledge. The proposed work introduces a big data framework to analyze such reviews and ratings, starting with data collection from different sources. Followed by integrating the collected data, which comes in different formats, toward the further processing phase. Finally, the analysis and visualization steps to draw the conclusions. Our work was tested on real data collected from active web resources.
KW - big data analytics
KW - data processing
KW - map-reduce
KW - sentiment analysis
KW - web scraping
KW - SENTIMENT CLASSIFICATION
UR - http://www.scopus.com/inward/record.url?scp=85104820388&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/432bd5e3-267f-36b6-90fd-2ea5fe64b19f/
U2 - 10.1109/elconrus51938.2021.9396606
DO - 10.1109/elconrus51938.2021.9396606
M3 - Conference contribution
AN - SCOPUS:85104820388
T3 - IEEE NW Russia Young Researchers in Electrical and Electronic Engineering Conference
SP - 185
EP - 190
BT - Proceedings of the 2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, ElConRus 2021
A2 - Shaposhnikov, S.
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, ElConRus 2021
Y2 - 26 January 2021 through 28 January 2021
ER -
ID: 76591168