期刊
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
卷 26, 期 2, 页码 1285-1296出版社
SPRINGER
DOI: 10.1007/s10586-022-03626-y
关键词
Big data; Business data analytics; Machine learning; Deep learning; Social media
This study proposes an application that detects satisfaction tone on social media, using machine learning and deep learning techniques. The study simplifies the concept of big data analytics for business on social media by using natural language processing tools. Evaluation of classifiers shows that the Random Forest classifier and Support Vector Machine achieve good results, while the Deep Neural Networks algorithm performs the best.
Satisfaction Detection is one of the most common issues that impact the business world. So, this study aims to suggest an application that detects the Satisfaction tone that leads to customer happiness for Big Data that came out from online businesses on social media, in particular, Facebook and Twitter, by using two famous methods, machine learning and deep learning (DL) techniques.There is a lack of datasets that are involved with this topic. Therefore, we have collected the dataset from social media. We have simplified the concept of Big Data analytics for business on social media using three of the most famous Natural Language Processing tools, stemming, normalization, and stop word removal. To evaluate the performance of the classifiers, we calculated F1-measure, Recall, and Precision measures. The result showed superiority for the Random Forest classifier the highest value of F1-measure with (99.1%). The best result achieved without applying pre-processing techniques, through Support Vector Machine with F1-measure (93.4%). On the other hand, we apply DL techniques, and we apply the feature extraction method, which includes Word Embedding and Bag of Words on the dataset. The results showed superiority for the Deep Neural Networks DNN algorithm.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据