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Improved exponential cuckoo search method for sentiment analysis
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AI
DOI：10.1007/s11042-022-14229-5.png)
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摘要
En 中文
Sentiment analysis is a type of contextual text mining that determines how people feel about emotional issues that are frequently discussed on social media. The sentiments of emotive data are analyzed using a variety of sentiment analysis approaches, including lexicon-based, machine learning-based, and hybrid methods. Unsupervised approaches, particularly clustering methods are preferred over other methods since they can be applied directly to unlabeled datasets. Therefore, a clustering method based on an improved exponential cuckoo search has been proposed in this study for sentiment analysis. The proposed clustering method finds the optimal cluster centers from emotive datasets, which are then utilized to determine the sentiment polarity of emotive contents. The proposed improved exponential cuckoo search is first tested on standard and CEC-2013 benchmark functions before being utilized to determine the best cluster centroids from sentimental datasets. To assess the efficiency of the proposed method, it has been 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 on nine sentimental datasets. The Experimental results and statistical analysis have proven the efficacy of the proposed method.
Keyword:
Sentimental data
Data processing
Feature extraction
Improved exponential cuckoo search
Clustering
期刊
IF:
3
论文数:
2.0W
被引数:
3.2W
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