Mohd Ridzwan Yaakub

Metaheuristic algorithms for feature selection in sentiment analysis

Sentiment analysis functions by analyzing and extracting opinions from documents, websites, blogs, discussion forums and others to identify sentiment patterns on opinions expressed by consumers. It analyzes peoples sentiment and identifies types of sentiment in comments expressed by consumers on certain matters. This paper highlights comparative studies on the types of feature selection in sentiment analysis based on natural language processing and modern methods such as Genetic Algorithm and Rough Set Theory. This study compares feature selection in text classification based on traditional and sentiment analysis methods. Feature selection is an important step in sentiment analysis because a suitable feature selection can identify the actual product features criticized or discussed by consumers. It can be concluded that metaheuristic based algorithms have the potential to be implemented in sentiment analysis research and can produce an optimal subset of features by eliminating features that are irrelevant and redundant.

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

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.

Statistical analysis for validating ACO-KNN algorithm as feature selection in sentiment analysis

This research paper aims to propose a hybrid of ant colony optimization (ACO) and k-nearest neighbor (KNN) algorithms as feature selections for selecting and choosing relevant features from customer review datasets. Information gain (IG), genetic algorithm (GA), and rough set attribute reduction (RSAR) were used as baseline algorithms in a performance comparison with the proposed algorithm. This paper will also discuss the significance test, which was used to evaluate the performance differences between the ACO-KNN, IG-GA, and IG-RSAR algorithms. This study evaluated the performance of the ACO-KNN algorithm using precision, recall, and F-score, which were validated using the parametric statistical significance tests. The evaluation process has statistically proven that this ACO-KNN algorithm has been significantly improved compared to the baseline algorithms. The evaluation process has statistically proven that this ACO-KNN algorithm has been significantly improved compared to the baseline algorithms. In addition, the experimental results have proven that the ACO-KNN can be used as a feature selection technique in sentiment analysis to obtain quality, optimal feature subset that can represent the actual data in customer review data.This research paper aims to propose a hybrid of ant colony optimization (ACO) and k-nearest neighbor (KNN) algorithms as feature selections for selecting and choosing relevant features from customer review datasets. Information gain (IG), genetic algorithm (GA), and rough set attribute reduction (RSAR) were used as baseline algorithms in a performance comparison with the proposed algorithm. This paper will also discuss the significance test, which was used to evaluate the performance differences between the ACO-KNN, IG-GA, and IG-RSAR algorithms. This study evaluated the performance of the ACO-KNN algorithm using precision, recall, and F-score, which were validated using the parametric statistical significance tests. The evaluation process has statistically proven that this ACO-KNN algorithm has been significantly improved compared to the baseline algorithms. The evaluation process has statistically proven that this ACO-KNN algorithm has been significantly improved compared to the baseline algorithms. In ...

A new method to discretize time to identify the milestones of online social networks

Online social networks (OSNs) are complex time-varying networks due to the exponential growth in the number of users and the activities of those users. As the form of OSNs can change in each time frame, those working in domains such as community detection, event detection, big data analytics, recommender systems and marketing need to find a way to discretize time to identify the behavioural changes in the OSN over time. For dynamic domains, it is necessary to chunk the network into some time windows and monitor all these time windows. However, to date, many studies have only attempted to monitor a network using one-time window as one inseparable piece of information, which can lead to misinterpretation of the data. Existing methods predict the population growth of a network based on a whole growth rate, but a network has some distinct growth rates during its lifespan. Therefore, this study aims to propose a new method to discretize time to detect the milestones of OSNs. However, many parameters can affect OSN growth. Therefore, in this study, an OSN growth equation is formulated on the basis that the network follows a specific order and discipline in its growth. This study introduces a two-variable equation based on the number of users and the number of connections, which are two common variables in all OSNs, to identify behavioural changes in OSNs. Experiments conducted on six different datasets as well as on real Facebook and real Twitter data show that an OSN follows two different patterns during its lifespan. These two growth patterns differ markedly, and the point at which these two patterns meet is the milestone of the network.