There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

It represents a universally applicable attitude and skill set everyone, not just computer scientists, would be eager to learn and use.

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Computational Thinking 計算論的思考

In recent years, unsupervised feature selection methods have raised considerable interest in many research areas; this is mainly due to their ability to identify and select relevant features without needing class label information. In this paper, we provide a comprehensive and structured review of the most relevant and recent unsupervised feature selection methods reported in the literature. We present a taxonomy of these methods and describe the main characteristics and the fundamental ideas they are based on. Additionally, we summarized the advantages and disadvantages of the general lines in which we have categorized the methods analyzed in this review. Moreover, an experimental comparison among the most representative methods of each approach is also presented. Finally, we discuss some important open challenges in this research area.

A review of unsupervised feature selection methods

Up-to-date report on the accuracy and efficiency of state-of-the-art classifiers.We compare the accuracy of 11 classification algorithms pairwise and groupwise.We examine separately the training, parameter-tuning, and testing time.GBDT and Random Forests yield highest accuracy, outperforming SVM.GBDT is the fastest in testing, Naive Bayes the fastest in training. Current benchmark reports of classification algorithms generally concern common classifiers and their variants but do not include many algorithms that have been introduced in recent years. Moreover, important properties such as the dependency on number of classes and features and CPU running time are typically not examined. In this paper, we carry out a comparative empirical study on both established classifiers and more recently proposed ones on 71 data sets originating from different domains, publicly available at UCI and KEEL repositories. The list of 11 algorithms studied includes Extreme Learning Machine (ELM), Sparse Representation based Classification (SRC), and Deep Learning (DL), which have not been thoroughly investigated in existing comparative studies. It is found that Stochastic Gradient Boosting Trees (GBDT) matches or exceeds the prediction performance of Support Vector Machines (SVM) and Random Forests (RF), while being the fastest algorithm in terms of prediction efficiency. ELM also yields good accuracy results, ranking in the top-5, alongside GBDT, RF, SVM, and C4.5 but this performance varies widely across all data sets. Unsurprisingly, top accuracy performers have average or slow training time efficiency. DL is the worst performer in terms of accuracy but second fastest in prediction efficiency. SRC shows good accuracy performance but it is the slowest classifier in both training and testing.

An up-to-date comparison of state-of-the-art classification algorithms

Now days, E-commerce systems have become extremely important. Large numbers of customers are choosing online shopping because of its convenience, reliability, and cost. Client generated information and especially item reviews are significant sources of data for consumers to make informed buy choices and for makers to keep track of customer’s opinions. It is difficult for customers to make purchasing decisions based on only pictures and short product descriptions. On the other hand, mining product reviews has become a hot research topic and prior researches are mostly based on pre-specified product features to analyse the opinions. Natural Language Processing (NLP) techniques such as NLTK for Python can be applied to raw customer reviews and keywords can be extracted. This paper presents a survey on the techniques used for designing software to mine opinion features in reviews. Elven IEEE papers are selected and a comparison is made between them. These papers are representative of the significant improvements in opinion mining in the past decade.

Mining Opinion Features in Customer Reviews.

Purpose




Users are the key players in an online social network (OSN), so the behavior of the OSN is strongly related to their behavior User weight refers to the influence of the users on the OSN The purpose of this paper is to propose a method to identify the user weight based on a new metric for defining the time intervals




Design/methodology/approach




The behavior of an OSN changes over time, thus the user weight in the OSN is different in each time frame Therefore, a good metric for estimating the user weight in an OSN depends on the accuracy of the metric used to define the time interval New metric for defining the time intervals is based on the standard deviation and identifies that the user weight is based on a simple exponential smoothing model




Findings




The results show that the proposed method covers the maximum behavioral changes of the OSN and is able to identify the influential users in the OSN more accurately than existing methods




Research limitations/implications




In event detection, when a terrorist attack occurs as an event, knowing the influential users help us to know the leader of the attack Knowing the influential user in each time interval based on this study can help us to detect communities which formed around these people Finally, in marketing, this issue helps us to have a targeted advertising




Practical implications




User effect is a significant issue in many OSN domain problems, such as community detection, event detection and recommender systems




Originality/value




Previous studies do not give priority to the recent time intervals in identifying the relative importance of users Thus, defining a metric to compute a time interval that covers the maximum changes in the network is a major shortcoming of earlier studies Some experiments were conducted on six different data sets to test the performance of the proposed model in terms of the computed time intervals and user weights

New time-based model to identify the influential users in online social networks

Nowadays, what user think is the most difficult and complicated task handled by organizations. The way to idetify the attitude of the speaker or a writer on some topics is to use sentiment analysis. The use of sentiment analysis is to identify user's opinion towards some topics whether it is positive or negative. This paper presents the techniques used by previous researchers in sentiment analysis which are Machine Learning and Natural Language Processing (NLP) in solving the classification task. The comparison among these two main approaches reveals that Machine Learning techniques can solve classification task with reasonable success and with very high accuracy compared to NLP-based techniques but it is depending on the training and test data with respect to the domain. This paper also presents the use of ontology in sentiment analysis that can help in achieving more high accuracy for the classification task.

https://google.iopscience.iop.org/article/10.1088/1757-899X/551/1/012070/pdf

A Review on Sentiment Analysis Techniques and Applications

There is a continued interest in understanding people’s interest through the contents they share online. However, the data generated is massive, characterized by textual jargons and tokens that contain no sentiment or opinion value. One way of reducing the data dimension and pruning of irrelevant features is feature selection. However, the existing approaches of feature selection are still inefficient. Two prominent feature selection methods in sentiment analysis are information gain and ontology-based methods. Information gain has the disadvantage of not considering redundancy between features while ontology-based approach requires a lot of human intervention. The aim of this paper is to review these two methods. The review of these two methods shows that using the two methods in a two-step approach can overcome their limitations and provide an optimal feature set for sentiment analysis.

https://www.accentsjournals.org/PaperDirectory/Journal/IJACR/2019/9/4.pdf

A review of feature selection in sentiment analysis using information gain and domain specific ontology

With the fast development of World Wide Web 2.0 has resulted in huge number of reviews where the consumers share their opinion about a variety of products in the websites, forum and social media such as Twitter and Instagram. For the organizations, they have to analyze customer’s behavior to find new market trends and insights. Sentiment analysis concept used to extract the positive, negative or neutral sentiment of the features from the unstructured data of product reviews. In this paper, we explore the techniques and tools used to enhance the ontology-based approach. Combination of ontology-based on Formal Concept Analysis (FCA) which a process of obtaining a formal ontology or a concept hierarchy from a group of objects with their properties and K-Nearest Neighbor (KNN) to classify the reviews. We believe with these techniques, we are able to view the strength and weakness of the product in more detail where the feature selection process will more be systematic and will result in the highest feature set.

Sentiment Analysis: An Enhancement of Ontological-Based Using Hybrid Machine Learning Techniques

As e-commerce is becoming more and more popular, the number of customer reviews that a product receives grows rapidly. In order to enhance customer satisfaction and their shopping experiences, it has become important to analysis customers reviews to extract opinions on the products that they buy. Thus, Opinion Mining is getting more important than before especially in doing analysis and forecasting about customers' behavior for businesses purpose. The right decision in producing new products or services based on data about customers' characteristics means profit for organization/company. This paper proposes a new architecture for Opinion Mining, which uses a multidimensional model to integrate customers' characteristics and their comments about products (or services). The key step to achieve this objective is to transfer comments (opinions) to a fact table that includes several dimensions, such as, customers, products, time and locations. This research presents a comprehensive way to calculate customers' orientation for all possible products' attributes.

Mohd Ridzwan Yaakub

Papers

New time-based model to identify the influential users in online social networks

A Review on Sentiment Analysis Techniques and Applications

A review of feature selection in sentiment analysis using information gain and domain specific ontology

Sentiment Analysis: An Enhancement of Ontological-Based Using Hybrid Machine Learning Techniques

Integration of Opinion into Customer Analysis Model