Abstract
As the innovation is developing immeasurably, the data is increases daily. This is used by the reviewers to identify the views regarding a particular thing and accordingly they decide their opinion on the basis of that reviews. Knowledge mining techniques has been used for extraction of elements from these datasets. The algorithm generated and tested can be used to find out technical words; hence we can expose them to appropriate class of technical words. Here we are only considering technical words in English language. We compare the results with the proposed approach. We calculate accuracy with random forest approach as well as bagging and find out that bagged random forest approach with Gini index for feature selection, i.e., proposed approach gives best result. With proposed approach, accuracy 80.64% in case of percentage split using 66 and 34 as training and testing percent and 86.81% in case of cross-validation model.
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Singhal, S., Maheshwari, S., Meena, M. (2020). Bagged Random Forest Approach to Classify Sentiments Based on Technical Words. In: Sharma, H., Pundir, A., Yadav, N., Sharma, A., Das, S. (eds) Recent Trends in Communication and Intelligent Systems. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-0426-6_11
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DOI: https://doi.org/10.1007/978-981-15-0426-6_11
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