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Journal : Journal of Information Systems Engineering and Business Intelligence

Sentiment Analysis in the Sales Review of Indonesian Marketplace by Utilizing Support Vector Machine Anang Anggono Lutfi; Adhistya Erna Permanasari; Silmi Fauziati
Journal of Information Systems Engineering and Business Intelligence Vol. 4 No. 1 (2018): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.4.1.57-64

Abstract

The online store is changing people’s shopping behavior. Despite the fact, the potential customer’s distrust in the quality of products and service is one of the online store’s weaknesses. A review is provided by the online stores to overcome this weakness. Customers often write a review using languages that are not well structured. Sentiment analysis is used to extract the polarity of the unstructured texts. This research attempted to do a sentiment analysis in the sales review. Sentiment analysis in sales reviews can be used as a tool to evaluate the sales. This research intends to conduct a sentiment analysis in the sales review of Indonesian marketplace by utilizing Support Vector Machine and Naive Bayes. The reviews of the data are gathered from one of Indonesian marketplace, Bukalapak. The data are classified into positive or negative class. TF-IDF is used to feature extraction. The experiment shows that Support Vector Machine with linear kernel provides higher accuracy than Naive Bayes. Support Vector Machine shows the highest accuracy average. The generated accuracy is 93.65%. This approach of sentiment analysis in sales review can be used as the base of intelligent sales evaluation for online stores in the future.
Corrigendum: Sentiment Analysis in the Sales Review of Indonesian Marketplace by Utilizing Support Vector Machine Anang Anggono Lutfi; Adhistya Erna Permanasari; Silmi Fauziati
Journal of Information Systems Engineering and Business Intelligence Vol. 4 No. 2 (2018): October
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (111.387 KB) | DOI: 10.20473/jisebi.4.2.169

Abstract

In the version of this article initially published, there were some errors in Section III, Methods and Section VI, Conclusions. In Preprocessing of Methods, there is a sentence “The informal words may be in the form of slang words or abbreviations that are often used in daily life like cp at (from “cepat” or fast), blum (from “belum” or not yet), and gak (from “tidak” or no).”. The correct sentence is “The informal words may be in the form of slang words or abbreviations that are often used in daily life like cpat (from “cepat” or fast), blum (from “belum” or not yet), and gak (from “tidak” or no).”. In Text Classification of Methods, there is a sentence “Where P(B|A) is the probability of B appearance when A is known? The value P(A|B) is the probability of an appearance if B is known. P(A) is the probability of an appearance, while P(B) is the probability of B appearance.”. The correct sentence is “Where P(B│A) is the probability of the appearance of B when A is known. The value of P(A|B) is the probability of the appearance of A if B is known. P(A) is the probability of the appearance of A, while P(B) is the probability of the appearance of B.”. In Conclusions, a sentence “The accuracy reaches 93.42%; using 25% features with highest TF-IDF” should be changed to “The accuracy reaches 93.65%; using 25% features with highest TF-IDF” based on the results in Fig.3. These errors have been corrected in the PDF versions of the article.