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A Fuzzy Approach to Sentiment Analysis at the Sentence Level
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Best Practices of Convolutional Neural Networks for Question Classification
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Hesitant Fuzzy Linguistic Preference Utility Set and Its Application in Selection of Fire Rescue Plans
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An interaction consensus in group decision making under distributed trust information
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Successes and challenges in developing a hybrid approach to sentiment analysis
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A minimum adjustment cost feedback mechanism based consensus model for group decision making under social network with distributed linguistic trust
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A consensus approach to the sentiment analysis problem driven by support-based IOWA majority
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Successes and challenges in developing a hybrid approach to sentiment analysis
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A Consensus Approach to the Sentiment Analysis Problem Driven by Support-Based IOWA Majority
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A Hybrid Approach to Sentiment Analysis with Benchmarking Results
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A Hybrid Approach to the Sentiment Analysis Problem at the Sentence Level
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Abstract:
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link. ; The objective of this article is to present a hybrid approach to the Sentiment Analysis problem at the sentence level. This new method uses natural language processing (NLP) essential techniques, a sentiment lexicon enhanced with the assistance of SentiWordNet, and fuzzy sets to estimate the semantic orientation polarity and its intensity for sentences, which provides a foundation for computing with sentiments. The proposed hybrid method is applied to three different data-sets and the results achieved are compared to those obtained using Naïve Bayes and Maximum Entropy techniques. It is demonstrated that the presented hybrid approach is more accurate and precise than both Naïve Bayes and Maximum Entropy techniques, when the latter are utilised in isolation. In addition, it is shown that when applied to datasets containing snippets, the proposed method performs similarly to state of the art techniques.
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Keyword:
Computing with Sentiments; Fuzzy sets; Maximum Entropy; Na ïve Bayes; Semantic rules; Sentiment Analysis; SentiWordNet; Unsupervised machine learning
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URL: http://hdl.handle.net/2086/12093 https://doi.org/10.1016/j.knosys.2016.05.040
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