Sentiment Analysis on Microblogging with K-Means Clustering and Artificial Bee Colony

2019 ◽  
Vol 18 (03) ◽  
pp. 1950017 ◽  
Author(s):  
Korawit Orkphol ◽  
Wu Yang
Keyword(s):  
Artificial Bee Colony ◽  
Clustering Algorithm ◽  
Global Optimum ◽  
High Dimensional ◽  
Local Optimum ◽  
Short Message ◽  
Initial State ◽  
Bee Colony ◽  
Analysis Technique ◽  
Value Decomposition

Microblogging is a type of blog used by people to express their opinions, attitudes, and feelings toward entities with a short message and this message is easily shared through the network of connected people. Knowing their sentiments would be beneficial for decision-making, planning, visualization, and so on. Grouping similar microblogging messages can convey some meaningful sentiments toward an entity. This task can be accomplished by using a simple and fast clustering algorithm, [Formula: see text]-means. As the microblogging messages are short and noisy they cause high sparseness and high-dimensional dataset. To overcome this problem, term frequency–inverse document frequency (tf–idf) technique is employed for selecting the relevant features, and singular value decomposition (SVD) technique is employed for reducing the high-dimensional dataset while still retaining the most relevant features. These two techniques adjust dataset to improve the [Formula: see text]-means efficiently. Another problem comes from [Formula: see text]-means itself. [Formula: see text]-means result relies on the initial state of centroids, the random initial state of centroids usually causes convergence to a local optimum. To find a global optimum, artificial bee colony (ABC), a novel swarm intelligence algorithm, is employed to find the best initial state of centroids. Silhouette analysis technique is also used to find optimal [Formula: see text]. After clustering into [Formula: see text] groups, each group will be scored by SentiWordNet and we analyzed the sentiment polarities of each group. Our approach shows that combining various techniques (i.e., tf–idf, SVD, and ABC) can significantly improve [Formula: see text]-means result (41% from normal [Formula: see text]-means).

scholarly journals Chaotic Artificial Bee Colony Algorithm for System Identification of a Small-Scale Unmanned Helicopter

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Li Ding ◽  
Hongtao Wu ◽  
Yu Yao
Keyword(s):  
System Identification ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
State Space Model ◽  
Global Optimum ◽  
Small Scale ◽  
Local Optimum ◽  
Unmanned Helicopter ◽  
Bee Colony ◽  
Chaotic Operator

The purpose of this paper is devoted to developing a chaotic artificial bee colony algorithm (CABC) for the system identification of a small-scale unmanned helicopter state-space model in hover condition. In order to avoid the premature of traditional artificial bee colony algorithm (ABC), which is stuck in local optimum and can not reach the global optimum, a novel chaotic operator with the characteristics of ergodicity and irregularity was introduced to enhance its performance. With input-output data collected from actual flight experiments, the identification results showed the superiority of CABC over the ABC and the genetic algorithm (GA). Simulations are presented to demonstrate the effectiveness of our proposed algorithm and the accuracy of the identified helicopter model.

scholarly journals Spatial information of fuzzy clustering based mean best artificial bee colony algorithm for phantom brain image segmentation

2021 ◽  
Vol 11 (5) ◽  
pp. 4050
Author(s):  
Waleed Alomoush ◽  
Ayat Alrosan ◽  
Ammar Almomani ◽  
Khalid Alissa ◽  
Osama A. Khashan ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Fuzzy Clustering ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Clustering Algorithm ◽  
Spatial Information ◽  
Medical Image Segmentation ◽  
Local Optimum ◽  
Brain Image ◽  
Bee Colony

Fuzzy c-means algorithm (FCM) is among the most commonly used in the medical image segmentation process. Nevertheless, the traditional FCM clustering approach has been several weaknesses such as noise sensitivity and stuck in local optimum, due to FCM hasn’t able to consider the information of contextual. To solve FCM problems, this paper presented spatial information of fuzzy clustering-based mean best artificial bee colony algorithm, which is called SFCM-MeanABC. This proposed approach is used contextual information in the spatial fuzzy clustering algorithm to reduce sensitivity to noise and its used MeanABC capability of balancing between exploration and exploitation that is explore the positive and negative directions in search space to find the best solutions, which leads to avoiding stuck in a local optimum. The experiments are carried out on two kinds of brain images the Phantom MRI brain image with a different level of noise and simulated image. The performance of the SFCM-MeanABC approach shows promising results compared with SFCM-ABC and other stats of the arts.

scholarly journals K-Means Clustering Algorithm Based on Chaotic Adaptive Artificial Bee Colony

Algorithms ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 53
Author(s):  
Qibing Jin ◽  
Nan Lin ◽  
Yuming Zhang
Keyword(s):  
Artificial Bee Colony ◽  
Clustering Algorithm ◽  
Heuristic Algorithms ◽  
Fitness Function ◽  
Step Length ◽  
Standard Test ◽  
Local Optimum ◽  
Bee Colony ◽  
Clustering Problems ◽  
Speed And Accuracy

K-Means Clustering is a popular technique in data analysis and data mining. To remedy the defects of relying on the initialization and converging towards the local minimum in the K-Means Clustering (KMC) algorithm, a chaotic adaptive artificial bee colony algorithm (CAABC) clustering algorithm is presented to optimally partition objects into K clusters in this study. This algorithm adopts the max–min distance product method for initialization. In addition, a new fitness function is adapted to the KMC algorithm. This paper also reports that the iteration abides by the adaptive search strategy, and Fuch chaotic disturbance is added to avoid converging on local optimum. The step length decreases linearly during the iteration. In order to overcome the shortcomings of the classic ABC algorithm, the simulated annealing criterion is introduced to the CAABC. Finally, the confluent algorithm is compared with other stochastic heuristic algorithms on the 20 standard test functions and 11 datasets. The results demonstrate that improvements in CAABA-K-means have an advantage on speed and accuracy of convergence over some conventional algorithms for solving clustering problems.

scholarly journals Artificial Bee Colony Algorithm Combined with Grenade Explosion Method and Cauchy Operator for Global Optimization

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Jian-Guo Zheng ◽  
Chao-Qun Zhang ◽  
Yong-Quan Zhou
Keyword(s):  
Global Optimization ◽  
Honey Bees ◽  
Artificial Bee Colony ◽  
Global Optimum ◽  
Local Optimum ◽  
Bee Colony ◽  
Explosion Method ◽  
Accelerate Convergence ◽  
Cauchy Operator ◽  
Intelligence Technique

Artificial bee colony (ABC) algorithm is a popular swarm intelligence technique inspired by the intelligent foraging behavior of honey bees. However, ABC is good at exploration but poor at exploitation and its convergence speed is also an issue in some cases. To improve the performance of ABC, a novel ABC combined with grenade explosion method (GEM) and Cauchy operator, namely, ABCGC, is proposed. GEM is embedded in the onlooker bees’ phase to enhance the exploitation ability and accelerate convergence of ABCGC; meanwhile, Cauchy operator is introduced into the scout bees’ phase to help ABCGC escape from local optimum and further enhance its exploration ability. Two sets of well-known benchmark functions are used to validate the better performance of ABCGC. The experiments confirm that ABCGC is significantly superior to ABC and other competitors; particularly it converges to the global optimum faster in most cases. These results suggest that ABCGC usually achieves a good balance between exploitation and exploration and can effectively serve as an alternative for global optimization.

Chaotic artificial bee colony approach to step planning of maintaining balance for quadruped robot

2014 ◽  
Vol 7 (2) ◽  
pp. 175-191 ◽  
Author(s):  
Qinan Luo ◽  
Haibin Duan
Keyword(s):  
Artificial Bee Colony ◽  
Contact Point ◽  
Optimization Method ◽  
Global Optimum ◽  
Quadruped Robot ◽  
Experimental Results ◽  
Local Optimum ◽  
Herd Behavior ◽  
Content Type ◽  
Bee Colony

Purpose – Artificial bee colony (ABC) algorithm is a relatively new optimization method inspired by the herd behavior of honey bees, which shows quite intelligence. The purpose of this paper is to propose an improved ABC optimization algorithm based on chaos theory for solving the push recovery problem of a quadruped robot, which can tune the controller parameters based on its search mechanism. ADAMS simulation environment is adopted to implement the proposed scheme for the quadruped robot. Design/methodology/approach – Maintaining balance is a rather complicated global optimum problem for a quadruped robot which is about seeking a foot contact point prevents itself from falling down. To ensure the stability of the intelligent robot control system, the intelligent optimization method is employed. The proposed chaotic artificial bee colony (CABC) algorithm is based on basic ABC, and a chaotic mechanism is used to help the algorithm to jump out of the local optimum as well as finding the optimal parameters. The implementation procedure of our proposed chaotic ABC approach is described in detail. Findings – The proposed CABC method is applied to a quadruped robot in ADAMS simulator. Using the CABC to implement, the quadruped robot can work smoothly under the interference. A comparison among the basic ABC and CABC is made. Experimental results verify a better trajectory tracking response can be achieved by the proposed CABC method after control parameters training. Practical implications – The proposed CABC algorithm can be easily applied to practice and can steer the robot during walking, which will considerably increase the autonomy of the robot. Originality/value – The proposed CABC approach is interesting for the optimization of a control scheme for quadruped robot. A parameter training methodology, using the presented intelligent algorithm is proposed to increase the learning capability. The experimental results verify the system stabilization, favorable performance and no chattering phenomena can be achieved by using the proposed CABC algorithm. And, the proposed CABC methodology can be easily extended to other applications.

scholarly journals Particle Swarm and Bacterial Foraging Inspired Hybrid Artificial Bee Colony Algorithm for Numerical Function Optimization

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Li Mao ◽  
Yu Mao ◽  
Changxi Zhou ◽  
Chaofeng Li ◽  
Xiao Wei ◽  
...  
Keyword(s):  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Function Optimization ◽  
Local Optimum ◽  
Search Range ◽  
Bacterial Foraging Optimization ◽  
Numerical Function ◽  
Bacterial Foraging ◽  
Bee Colony

Artificial bee colony (ABC) algorithm has good performance in discovering the optimal solutions to difficult optimization problems, but it has weak local search ability and easily plunges into local optimum. In this paper, we introduce the chemotactic behavior of Bacterial Foraging Optimization into employed bees and adopt the principle of moving the particles toward the best solutions in the particle swarm optimization to improve the global search ability of onlooker bees and gain a hybrid artificial bee colony (HABC) algorithm. To obtain a global optimal solution efficiently, we make HABC algorithm converge rapidly in the early stages of the search process, and the search range contracts dynamically during the late stages. Our experimental results on 16 benchmark functions of CEC 2014 show that HABC achieves significant improvement at accuracy and convergence rate, compared with the standard ABC, best-so-far ABC, directed ABC, Gaussian ABC, improved ABC, and memetic ABC algorithms.

scholarly journals A Genetic XK-Means Algorithm with Empty Cluster Reassignment

Symmetry ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 744 ◽  
Author(s):  
Chun Hua ◽  
Feng Li ◽  
Chao Zhang ◽  
Jie Yang ◽  
Wei Wu
Keyword(s):  
Real World ◽  
Numerical Experiments ◽  
Clustering Algorithm ◽  
Global Optimum ◽  
Genetic Mechanism ◽  
Local Optimum ◽  
Clustering Problems ◽  
Initial Choice

K-Means is a well known and widely used classical clustering algorithm. It is easy to fall into local optimum and it is sensitive to the initial choice of cluster centers. XK-Means (eXploratory K-Means) has been introduced in the literature by adding an exploratory disturbance onto the vector of cluster centers, so as to jump out of the local optimum and reduce the sensitivity to the initial centers. However, empty clusters may appear during the iteration of XK-Means, causing damage to the efficiency of the algorithm. The aim of this paper is to introduce an empty-cluster-reassignment technique and use it to modify XK-Means, resulting in an EXK-Means clustering algorithm. Furthermore, we combine the EXK-Means with genetic mechanism to form a genetic XK-Means algorithm with empty-cluster-reassignment, referred to as GEXK-Means clustering algorithm. The convergence of GEXK-Means to the global optimum is theoretically proved. Numerical experiments on a few real world clustering problems are carried out, showing the advantage of EXK-Means over XK-Means, and the advantage of GEXK-Means over EXK-Means, XK-Means, K-Means and GXK-Means (genetic XK-Means).

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