Graphbased SemiSupervised Classification for Online Customer Reviews Using Consensus Clustering The purpose of this research is to present a graphbased semisupervised learning (GBSSL) method with high classification accuracy for handling a large number of customer reviews. Following recent developments in information and communication technology, it has become essential for companies to employ efficient methods for analyzing customer reviews for improving their products and services. In analyzing these reviews, it is necessary that they are classified based on the review content. However there are only a few labelled customer reviews that can be used for the purpose of classification. Semisupervised learning is effective in such case. In this research, we introduce GBSSL and show how it improves classification accuracy with the use of unsupervised clustering. The proposed learning method defines the result of consensus clustering based on the similarity between nodes. This research classifies customer reviews for a beauty salon using the proposed method and compares its accuracy with that of other machine learning methods to demonstrate the formeru0027s effectiveness.

Consistency Meets Inconsistency: A Unified Graph Learning Framework for Multiview Clustering Graph Learning has emerged as a promising technique for multiview clustering, and has recently attracted lots of attention due to its capability of adaptively learning a unified and probably better graph from multiple views. However, the existing multiview graph learning methods mostly focus on the multiview consistency, but neglect the potential multiview inconsistency (which may be incurred by noise, corruptions, or viewspecific characteristics). To address this, this paper presents a new graph learningbased multiview clustering approach, which for the first time, to our knowledge, simultaneously and explicitly formulates the multiview consistency and the multiview inconsistency in a unified optimization model. To solve this model, a new alternating optimization scheme is designed, where the consistent and inconsistent parts of each singleview graph as well as the unified graph that fuses the consistent parts of all views can be iteratively learned. It is noteworthy that our multiview graph learning model is applicable to both similarity graphs and dissimilarity graphs, leading to two graph fusionbased variants, namely, distance (dissimilarity) graph fusion and similarity graph fusion. Experiments on various multiview datasets demonstrate the superiority of our approach. The MATLAB source code is available at https:github.comyouweiliangConsistentGraphLearning.