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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 4 of 4 1 Successes and challenges in developing a hybrid approach to sentiment analysis Fujita, Hamido; Chiclana, Francisco; Appel, Orestes. - : Springer International Publishing, 2017 BASE Show details 2 A consensus approach to the sentiment analysis problem driven by support-based IOWA majority Appel, Orestes; Chiclana, Francisco; Carter, Jenny. - : John Wiley & Sons, Inc., 2017 BASE Show details 3 A Hybrid Approach to Sentiment Analysis with Benchmarking Results Appel, Orestes; Fujita, Hamido; Chiclana, Francisco. - : Springer Lectures Notes Computer Science, 2016 BASE Show details 4 A Hybrid Approach to the Sentiment Analysis Problem at the Sentence Level Appel, Orestes; Chiclana, Francisco; Carter, Jenny; Fujita, Hamido. - : Elsevier, 2016 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. Keyword: Computing with Sentiments; Fuzzy sets; Maximum Entropy; Na ïve Bayes; Semantic rules; Sentiment Analysis; SentiWordNet; Unsupervised machine learning URL: http://hdl.handle.net/2086/12093 https://doi.org/10.1016/j.knosys.2016.05.040 BASE Hide details

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