Abstract
We live in an age of massive online business, e-governance, and e-learning. All these activities involve transactions between customers, businessman, service providers, and recipients. Usually, the recipients give some comments on the quality of products and services. In this study, we proposed an algorithm named ASSAY (which means Analysis), to find the polarity at the document level. In our algorithm, initially we classify the reviews of each domain using naive Bayes and Support Vector Machine (SVM) algorithms which are in machine learning approach and then find the polarity at document level using HARN’s algorithm which comes under lexicon-based approach. In this algorithm, we use TextBlob for Parts of Speech (POS) tagging, where NV-Dictionary, ordinary dictionary, and SentiWordNet are used for extracting the polarities of features. Here, we combine both machine learning and lexicon-based approaches to calculate the result at document level accurately. In this way, we get the result about 80–85% more accurately than HARN’s algorithm which is proposed in lexicon-based approach.
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Devi, D.V.N., Venkata Rajini Kanth, T., Mounika, K., Sowjanya Swathi, N. (2019). Assay: Hybrid Approach for Sentiment Analysis. In: Satapathy, S., Joshi, A. (eds) Information and Communication Technology for Intelligent Systems . Smart Innovation, Systems and Technologies, vol 106. Springer, Singapore. https://doi.org/10.1007/978-981-13-1742-2_30
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DOI: https://doi.org/10.1007/978-981-13-1742-2_30
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