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- W2894916285 abstract "The field of sentiment analysis identifies and analyzes the public’s favorability in terms of an entity. The advent of social media websites and e-commerce applications has enhanced the significance of analysis of user reviews for the product manufacturers to obtain insights into the acceptability of products. Aspect-level sentiment analysis helps them gain better understanding of the acceptance of different features of the products in specific. This work presents a workflow model of topic extraction (aspect detection) and sentiment detection of user reviews using frequency-based approach and unsupervised machine learning methods over 7.8 million reviews of Amazon users. The work also employs methods to estimate the accuracy of the analytical model using the classical methods like logistic regression, support vector machine, and naive Bayesian approach." @default.
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- W2894916285 date "2018-10-04" @default.
- W2894916285 modified "2023-09-24" @default.
- W2894916285 title "Topic Modelling for Aspect-Level Sentiment Analysis" @default.
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- W2894916285 doi "https://doi.org/10.1007/978-981-13-1610-4_23" @default.
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