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Swarm optimization clustering methods for opinion mining
- Source :
- Natural Computing. 19:547-575
- Publication Year :
- 2018
- Publisher :
- Springer Science and Business Media LLC, 2018.
-
Abstract
- Supervised machine learning and opinion lexicon are the most frequent approaches for opinion mining, but they require considerable effort to prepare the training data and to build the opinion lexicon, respectively. In this paper, a novel unsupervised clustering approach is proposed for opinion mining. Three swarm algorithms based on Particle Swarm Optimization are evaluated using three corpora with different levels of complexity with respect to size, number of opinions, domains, languages, and class balancing. K-means and Agglomerative clustering algorithms, as well as, the Artificial Bee Colony and Cuckoo Search swarm-based algorithms were selected for comparison. The proposed swarm-based algorithms achieved better accuracy using the word bigram feature model as the pre-processing technique, the Global Silhouette as optimization function, and on datasets with two classes: positive and negative. Although the swarm-based algorithms obtained lower result for datasets with three classes, they are still competitive considering that neither labeled data, nor opinion lexicons are required for the opinion clustering approach.
- Subjects :
- Computer science
business.industry
Bigram
Sentiment analysis
Particle swarm optimization
Swarm behaviour
0102 computer and information sciences
02 engineering and technology
Machine learning
computer.software_genre
01 natural sciences
Feature model
Computer Science Applications
Hierarchical clustering
ComputingMethodologies_PATTERNRECOGNITION
010201 computation theory & mathematics
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Artificial intelligence
business
Cluster analysis
Cuckoo search
computer
Subjects
Details
- ISSN :
- 15729796 and 15677818
- Volume :
- 19
- Database :
- OpenAIRE
- Journal :
- Natural Computing
- Accession number :
- edsair.doi...........d1c84bebc7c4407a420e5b13a0960c7a