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- W4285263487 abstract "A major subset of natural language processing at intersection of computer linguistics and text mining is sentiment analysis that makes an effort of encapsulating the broader emotion from the accessible public opinions. The negations have a crucial role in linguistics in context of sentiment analysis as negations have the ability to impact the polarity of the other textual constituents. In a sentence, presence of negation does not impact only the word after the negation but its scope can extend up to a series of words next to the negation depending upon certain criteria. Considering broader level of structural formation negations can exist primarily in two ways, namely syntactic negation and morphological negation. Some common methods followed same strategy for negation handling without taking into consideration the difference among various kinds of negations. This work suggests a technique of handling ‘NOT’ which is a specific case of syntactic negation. Different linguistic characteristics are taken into account during designing and developing the technique. First sentiment analysis is done using some common classification models on several datasets. Then the datasets are processed with the ‘NOT’ handling technique and after that the same classification models are employed again. Performance is compared in both the cases with respect to different performance metrics by making use of 10 fold cross validation to show the efficacy of the ‘NOT’ handling strategy." @default.
- W4285263487 created "2022-07-14" @default.
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- W4285263487 date "2022-01-01" @default.
- W4285263487 modified "2023-10-16" @default.
- W4285263487 title "A Simple Strategy for Handling ‘NOT’ Can Improve the Performance of Sentiment Analysis" @default.
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- W4285263487 doi "https://doi.org/10.1007/978-981-19-3089-8_25" @default.
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