Big Data, Machine Learning, WebTechnology, Statistical Modelling
BOOK CHAPTERSP Sagar, K Oliullah, K Sohan, MFK Patwary, PRCMLA: Product Review Classification Using Machine Learning Algorithms, International Conference on Trends in Computational and Cognitive Engineering, 978-981-33-4673-4, pp.65-75, Jahangirnagar University, 2020. doi: https://doi.org/10.1007/978-981-33-4673-4_6
In our modern era, where the Internet is ubiquitous, everyone relies on various online resources for shopping and the increase in the use of social media platforms like Facebook, Twitter, etc. The user review spread rapidly among millions of users within a brief period. Consumer reviews on online products play a vital role in the selection of a product. The customer reviews are the measurement of customer satisfaction. This review data in terms of text can be analyzed to identify customers’ sentiment and demands. In this paper, we wish to perform four different classification techniques for various reviews available online with the help of artificial intelligence, natural language processing (NLP), and machine learning concepts. Moreover, a Web crawling methodology has also been proposed. Using this Web crawling algorithm, we can collect data from any website. We investigate and compare these …Md Mahfuzur Rahman, Sheikh Shah Mohammad Motiur Rahman, Shaikh Muhammad Allayear, Md Fazlul Karim Patwary, Md Tahsir Ahmed Munna, A Sentiment Analysis Based Approach for Understanding the User Satisfaction on Android Application, Data Engineering and Communication Technology, Advances in Intelligent Systems and Computing book series, AISC, volume 1079, pp.397-407, Hyderabad, India, 2020. doi: https://doi.org/10.1007/978-981-15-1097-7_33
The consistency of user satisfaction on mobile application has been more competitive because of the rapid growth of multi-featured applications. The analysis of user reviews or opinions can play a major role to understand the user’s emotions or demands. Several approaches in different areas of sentiment analysis have been proposed recently. The main objective of this work is to assist the developers in identifying the user’s opinion on their apps whether positive or negative. A sentiment analysis based approach has been proposed in this paper. NLP-based techniques Bags-of-Words, N-Gram, and TF-IDF along with Machine Learning Classifiers, namely, KNN, Random Forest (RF), SVM, Decision Tree, Naive Byes have been used to determine and generate a well-fitted model. It’s been found that RF provides 87.1% accuracy, 91.4% precision, 81.8% recall, 86.3% F1-Score. 88.9% of accuracy, 90.8% of precision, 86.4% of recall, and 88.5% of F1-Score are obtained from SVM.