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Enhancing sentiment analysis using Roulette wheel selection based cuckoo search clustering method

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Abstract

Sentiment analysis is a type of contextual text mining that assesses how users feel about emotive topics that are frequently discussed on social media. To analyze the sentiments of the textual data, a number of sentiment analysis methods such as lexicon-based, machine learning-based, and hybrid methods have been proposed. Among all methods, unsupervised methods, especially clustering methods are generally preferred, as they can directly be applied over the unlabelled datasets. Therefore, in this paper, a roulette wheel-based cuckoo search clustering method has been proposed for sentiment analysis. The proposed clustering method finds the optimal cluster centroids from the contents of sentimental datasets which are further used for determining the sentiment polarity of a document. The efficiency of the proposed roulette wheel cuckoo search clustering method has been evaluated on nine sentimental datasets including Twitter and Spam review datasets and compared with K-means, cuckoo search, grey wolf optimizer, grey wolf optimizer with simulated annealing, hybrid step size based cuckoo search, and spiral cuckoo search. The experimental analysis shows that the proposed methods attain the best mean accuracy, mean precision, and mean recall over 80% of the datasets. To statistically validate the efficacy of the proposed approach, box plots and paired t-test are also carried out. From the statistical analysis and experimental findings, the efficacy of the proposed method can be observed. The proposed clustering approach has theoretical implications for further studies to examine the sentimental data. Furthermore, the proposed method has significant practical implications for establishing a system that can generate conclusive comments on any societal issue.

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  1. PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, India

    Avinash Chandra Pandey & Deep Shikha Shukla

  2. Government Polytechnic College, Bundi, India

    Ankur Kulhari & Deep Shikha Shukla

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  1. Avinash Chandra Pandey
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Pandey, A.C., Kulhari, A. & Shukla, D.S. Enhancing sentiment analysis using Roulette wheel selection based cuckoo search clustering method. J Ambient Intell Human Comput 13, 1–29 (2022). https://doi.org/10.1007/s12652-021-03603-0

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