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Swarm optimization clustering methods for opinion mining

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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.

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Acknowledgements

Ellen Souza and Alisson Silva are supported by FACEPE (Pernambuco Research Foundation). Adriano L. I. Oliveira, Diego Santos, and Gustavo Oliveira are supported by CNPq (Brazilian Council for Scientific and Technological Development).

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Authors and Affiliations

  1. Center of Informatics, Federal University of Pernambuco (CIn-UFPE), Recife, Pernambuco, Brazil

    Ellen Souza, Diego Santos, Gustavo Oliveira, Alisson Silva & Adriano L. I. Oliveira

  2. MiningBR Research Group, Federal Rural University of Pernambuco (UFRPE), Recife, Pernambuco, Brazil

    Ellen Souza & Diego Santos

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  1. Ellen Souza
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  3. Gustavo Oliveira
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  5. Adriano L. I. Oliveira
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Correspondence to Ellen Souza.

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Souza, E., Santos, D., Oliveira, G. et al. Swarm optimization clustering methods for opinion mining. Nat Comput 19, 547–575 (2020). https://doi.org/10.1007/s11047-018-9681-2

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