The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Aspect-Based and Multi-Level Sentiment Information Applying Contrast Dictionary
Abstract
The customer feedbacks provide alternative and important sources to discover knowledge supporting the marketers and customers to make better decisions. However, the manual process to extract useful information depends on domain experts. This paper focuses on improving the performance of the automatic sentiment information extraction from customer feedbacks. The article proposes a new extraction method that consider multiple dimensions of feedback information, aspect, word, contrast, sentence or phrase, and document levels. The aspect-based sentiment extraction uses a named entity recognition technique to extract the desired aspects of a target product. The aspect-based sentiment combines with sentiment information from multiple levels of feedback contexts resulting in the fused sentiment information improves the extraction performance. We validate the effectiveness by measuring the accuracy of the sentiment and aspect recognition methods comparing with SentiStrength and Word-Count. This information gives some insights on customer satisfaction and can be applied in an alarming tool.
Related Content
|
Muath AlShaikh, Waleed Alsemaih, Sultan Alamri, Qusai Ramadan.
© 2024.
19 pages.
|
|
Anna M. Segooa, Billy M. Kalema.
© 2024.
27 pages.
|
|
Utsav Upadhyay, Alok Kumar, Gajanand Sharma, Ashok Kumar Saini, Varsha Arya, Akshat Gaurav, Kwok Tai Chui.
© 2024.
30 pages.
|
|
Yuan Ren.
© 2024.
8 pages.
|
|
Jon A. Chilingerian, Mitchell P. V. Glavin.
© 2024.
27 pages.
|
|
Hadeel Al-Obaidy, Aysha Ebrahim, Ali Aljufairi, Ahmed Mero, Omar Eid.
© 2024.
19 pages.
|
|
Ahmad Althunibat, Bayan Alsawareah, Siti Sarah Maidin, Belal Hawashin, Iqbal Jebril, Belal Zaqaibeh, Haneen A. Al-khawaja.
© 2024.
19 pages.
|
|
|