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A Novel Hybrid Algorithm for Sentiment Analysis via Classifier Ensembles for Online Shops User Using UserGenerated Contents and Review

Ghorbanian, Fereshteh; Jalali, Mehrdad

Abstract (englisch):
Recently, Sentiment analysis and classification on social networking has been becoming popular in recent years. Industry and companies have realized the value of huge data to create a valuable advantage to get more customer. User generated content in online reviews for online shops or social media makes a lot of brand related information for marketing fields. In this paper we proposed a method to classify the sentiment polarities and find customer opinions and feeling about everything to propose product selection for each user in online markets. Our qualitative and quantitative experiment shown the usefulness of using positive, neutral, and negative customer opinion for product recommendation in online markets. By considering different combinations of techniques such as feature hashing, bag of words, and lexicons, and also consider the extensive results that described in the literature for application purposes, we can present the accuracy and precision of our method for online markets users.

DOI: 10.1145/3404555.3404645
Zugehörige Institution(en) am KIT Institut für Nanotechnologie (INT)
Publikationstyp Proceedingsbeitrag
Publikationsmonat/-jahr 04.2020
Sprache Englisch
Identifikator ISBN: 978-1-4503-7708-9
KITopen-ID: 1000123009
Erschienen in 2020 6th International Conference on Computing and Artificial Intelligence (ICCAI 2020), Tianjin, China, 23rd - 26th April 2020
Verlag ACM, New York (NY)
Seiten 165–169
Schlagwörter Big data; sentiment analysis; classifier ensembles; marketing decisions; feature selection.
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