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Sentiment analysis is a process of analyzing, processing, concluding, and inferencing subjective texts with the sentiment. Companies use sentiment analysis for understanding public opinion, performing market research, analyzing brand reputation, recognizing customer experiences, and studying social media influence. According to the different needs for aspect granularity, it can be divided into document, sentence, and aspect-based ones. This article summarizes the recently proposed methods to solve an aspect-based sentiment analysis problem. At present, there are three mainstream methods: lexicon-based, traditional machine learning, and deep learning methods. In this survey article, we provide a comparative review of state-of-the-art deep learning methods. Several commonly used benchmark data sets, evaluation metrics, and the performance of the existing deep learning methods are introduced. Finally, existing problems and some future research directions are presented and discussed.

Imbalanced data is one type of datasets that are frequently found in real-world applications, e.g., fraud detection and cancer diagnosis. For this type of datasets, improving the accuracy to identify their minority class is a critically important issue. Feature selection is one method to address this issue. An effective feature selection method can choose a subset of features that favor in the accurate determination of the minority class. A decision tree is a classifier that can be built up by using different splitting criteria. Its advantage is the ease of detecting which feature is used as a splitting node. Thus, it is possible to use a decision tree splitting criterion as a feature selection method. In this paper, an embedded feature selection method using our proposed weighted Gini index &#x0028 WGI &#x0029 is proposed. Its comparison results with Chi2, F-statistic and Gini index feature selection methods show that F-statistic and Chi2 reach the best performance when only a few features are selected. As the number of selected features increases, our proposed method has the highest probability of achieving the best performance. The area under a receiver operating characteristic curve &#x0028 ROC AUC &#x0029 and F-measure are used as evaluation criteria. Experimental results with two datasets show that ROC AUC performance can be high, even if only a few features are selected and used, and only changes slightly as more and more features are selected. However, the performance of Fmeasure achieves excellent performance only if 20 &#x0025 or more of features are chosen. The results are helpful for practitioners to select a proper feature selection method when facing a practical problem.

In this paper, a novel method of point clouds segmentation based on Euclidean clustering and multi-plane extraction is newly proposed. To cope with overhanging objects, such as tree branches, a hybrid elevation map assisted with Euclidean clustering is designed. By clustering the 3D point clouds falling into the grid cell, the obstacles above a free space are checked and the corresponding traversable regions below are identified. Furthermore, the time consumption is reduced for the segementation by using the multi-resolution grids method. In addition, the multiplane extraction method based on RANSAC is well adapted to non-flat terrain. In the simulation, a variety of virtual environments are built on Gazebo platform to demonstrate the performance of the proposed algorithm. Moreover, it is also evaluated in the field environments. The results show that the accuracy as well as efficiency of point clouds segmentation achieves superior performance over existing approaches.

An imbalanced class problem occurs within abundant real-world applications, e.g., fraud detection, text classification, and cancer diagnosis. Beside balancing the imbalanced data distribution to deal with imbalanced data problems, another significant way to solve the bias-to-majority problem is via proper feature selection. This work is intended to use a feature selection method that can choose a subset of features and make ROC AUC and F-measure results in order to achieve high performance on a minority class. In this paper, a weighted Gini index(WGI) feature selection method is proposed. In order to evaluate the proposed method, a comparison result among Chi-square, F-statistic and Gini index feature selection is shown, and Xgboost is the classifier that is used to test the performance of the subset of features. Experimental results indicate that F-statistic contains the best performance when a few features are selected. However, when the number of selected features increases, WGI feature selection achieves the best results. A comparison between the average results from ROC AUC and F-measure are also presented. It shows that ROC AUC always contains a good performance, even if only a few features are selected, and only changes slightly as the subset of features expands. However, the performance of F-measure achieves a good performance after 60% of features are chosen. The results are helpful for practitioners to select a proper feature selection method when facing a practical problem.

A class imbalance problem often appears in many real world applications, e.g. fault diagnosis, text categorization, fraud detection. When dealing with a large-scale imbalanced dataset, feature selection becomes a great challenge. To confront it, this work proposes a feature selection approach based on a decision tree rule. The effectiveness of the proposed approach is verified by classifying a large-scale dataset from Santander Bank. The results show that our approach can achieve higher Area Under the Curve (AUC) and less computational time. We also compare it with filter-based feature selection approaches, i.e., Chi-Square and F-statistic. The results show that it outperforms them but needs slightly more computational efforts.