scholarly journals Design of Ensemble Classifier Selection Framework Based on Ant Colony Optimization for Sentiment Analysis and Opinion Mining

2019 ◽  
Vol 8 (12) ◽  
pp. 5112-5117
Keyword(s):  
Sentiment Analysis ◽  
Ant Colony Optimization ◽  
Opinion Mining ◽  
Ensemble Classifier ◽  
Ant Colony ◽  
Experimental Result ◽  
Ensemble Classifiers ◽  
Classifier Selection ◽  
Selection For ◽  
Selection Framework

Ensemble Classifier provides a promising way to improve the accuracy of classification for sentiment analysis and opinion mining. Ensemble classifier should combine with diverse base classifiers. However, establishing a connection between diversity and accuracy of ensemble classifier is tedious task because of sensitivity between diversity and accuracy. In this paper an Ensemble classifier selection (ECS) framework based on Ant Colony Optimization (ACO) algorithm is presented. The framework provides a subset of base classifiers from a given set of classifiers with maximum possible diversity and accuracy to design an ensemble classifier for sentiment analysis and opinion mining. This framework uses diversity measures and accuracy as selection criteria for classifier selection for ensemble creation. The experimental result shows that the ensemble classifiers provided by this framework presents an efficient way for sentiment analysis and opinion mining.

scholarly journals Design of adaptive ensemble classifier for online sentiment analysis and opinion mining

2021 ◽  
Vol 7 ◽  
pp. e660
Author(s):  
Sanjeev Kumar ◽  
Ravendra Singh ◽  
Mohammad Zubair Khan ◽  
Abdulfattah Noorwali
Keyword(s):  
Sentiment Analysis ◽  
False Positive ◽  
Opinion Mining ◽  
Negative Impact ◽  
Concept Drift ◽  
Data Distribution ◽  
Detection Algorithm ◽  
Ensemble Classifier ◽  
Detection Algorithms ◽  
Highly Sensitive

DataStream mining is a challenging task for researchers because of the change in data distribution during classification, known as concept drift. Drift detection algorithms emphasize detecting the drift. The drift detection algorithm needs to be very sensitive to change in data distribution for detecting the maximum number of drifts in the data stream. But highly sensitive drift detectors lead to higher false-positive drift detections. This paper proposed a Drift Detection-based Adaptive Ensemble classifier for sentiment analysis and opinion mining, which uses these false-positive drift detections to benefit and minimize the negative impact of false-positive drift detection signals. The proposed method creates and adds a new classifier to the ensemble whenever a drift happens. A weighting mechanism is implemented, which provides weights to each classifier in the ensemble. The weight of the classifier decides the contribution of each classifier in the final classification results. The experiments are performed using different classification algorithms, and results are evaluated on the accuracy, precision, recall, and F1-measures. The proposed method is also compared with these state-of-the-art methods, OzaBaggingADWINClassifier, Accuracy Weighted Ensemble, Additive Expert Ensemble, Streaming Random Patches, and Adaptive Random Forest Classifier. The results show that the proposed method handles both true positive and false positive drifts efficiently.

Sentiment Analysis of Social Networking Websites using Gravitational Search Optimization Algorithm

2018 ◽  
Vol 9 (1) ◽  
pp. 76-85 ◽  
Author(s):  
Lavika Goel ◽  
Anubhav Garg
Keyword(s):  
Social Networking ◽  
Sentiment Analysis ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Online Communities ◽  
Ant Colony ◽  
Algorithmic Approach ◽  
Search Optimization ◽  
Gravitational Search ◽  
Law Of Gravity

Analysing sentiments of various online communities have become now an interesting topic of research and industry. The behaviour of online communities resembles that of a swarm. This article presents a Gravitational Search algorithmic approach for sentiment analysis of online communities, and an optimization algorithm which is based on the mass interactions and law of gravity. In the end, the authors present comparisons with other techniques, particularly ant colony optimization and Naive Bayes classification for sentiment analysis.

scholarly journals Multicriteria-Based Crowd Selection Using Ant Colony Optimization

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Guan Wang ◽  
Farhad Ali ◽  
Jonghoon Yang ◽  
Shah Nazir ◽  
Ting Yang ◽  
...  
Keyword(s):  
Ant Colony Optimization ◽  
Ant Colony ◽  
Market Place ◽  
Effective Activity ◽  
Single Feature ◽  
Proposed Model ◽  
Efficiency And Effectiveness ◽  
Selection For ◽  
Collaboration And Communication ◽  
Selection Of

Internet-enabled technologies have provided a way for people to communicate and collaborate with each other. The collaboration and communication made crowdsourcing an efficient and effective activity. Crowdsourcing is a modern paradigm that employs cheap labors (crowd) for accomplishing different types of tasks. The task is usually posted online as an open call, and members of the crowd self-select a task to be carried out. Crowdsourcing involves initiators or crowdsourcers (an entity usually a person or an organization who initiate the crowdsourcing process and seek out the ability of crowd for a task), the crowd (online participant who is a having a particular background, qualification, and experience for accomplishing task in crowdsourcing activity), crowdsourcing task (the activity in which the crowd contribute), the process (how the activity is carried out), and the crowdsourcing platform (software or market place) where requesters offer various tasks and crowd workers complete these tasks. As the crowdsourcing is carried out in the online environment, it gives rise to certain challenges. The major problem is the selection of crowd that is becoming a challenging issue with the growth in crowdsourcing popularity. Crowd selection has been significantly investigated in crowdsourcing processes. Nonetheless, it has observed that the selection is based only on a single feature of the crowd worker which was not sufficient for appropriate crowd selection. For addressing the problem of crowd selection, a novel “ant colony optimization-based crowd selection method” (ACO-CS) is presented in this paper that selects a crowd worker based on multicriteria features. By utilizing the proposed model, the efficiency and effectiveness of crowdsourcing activity will be increased.

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