Abstract
Sentiment analysis and classification task is used in recommender systems to analyze movie reviews, tweets, Facebook posts, online product reviews, blogs, discussion forums, and online comments in social networks. Usually, the classification is performed using supervised machine learning methods such as support vector machine (SVM) classifier, which have many distinct parameters. The selection of the values for these parameters can greatly influence the classification accuracy and can be addressed as an optimization problem. Here we analyze the use of three heuristics, nature-inspired optimization techniques, cuckoo search optimization (CSO), ant lion optimizer (ALO), and polar bear optimization (PBO), for parameter tuning of SVM models using various kernel functions. We validate our approach for the sentiment classification task of Twitter dataset. The results are compared using classification accuracy metric and the Nemenyi test.
Highlights
IntroductionSentiment analysis [1,2,3] is a highly relevant research in the area of text analysis and mining
Sentiment analysis [1,2,3] is a highly relevant research in the area of text analysis and mining.Many people post their views, opinion and ideas in unstructured format
The best average accuracy was achieved by power series (n=5) kernel-0.9088, followed by sigmoid-0.8780, cubic polynomial (0.8604), Radial basis function (RBF) (0.8385), and linear (0.8070) kernels
Summary
Sentiment analysis [1,2,3] is a highly relevant research in the area of text analysis and mining. Many people post their views, opinion and ideas in unstructured format. Millions and billions of people and public are using social network websites such as Facebook, Twitter, and RenRen. The social media generates a huge volume of sentiment data in the various forms such as tweet id, status updates, reviews, author, content, tweets type and tweets status update. As the data size is going larger and larger, it is necessary to analyze and categorize the sentiment reviews or opinion of the various people to predict
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