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Linguistic Features for Subjectivity Classification

Authors: Huong Nguyen Thi Xuan; Anh-Cuong Le 0001; Le Minh Nguyen 0001;

Linguistic Features for Subjectivity Classification

Abstract

Opinions are subjective expressions that describe people's viewpoints, perspectives or feelings about entities, events and theirs properties. Detecting subjective expressions is the task of identifying whether a given text is subjective (i.e. an opinion)or objective (i.e. a reports fact). This task is considered as the first problem and it is very important for opinion mining and sentiment analysis which is now attracting many researchers cause its applicable capacity. Improvements in subjectivity classification will positively impact on the performance of a sentiment analysis system. Actually, features play the most important role for getting accurate subjective sentences. In this paper, we will enrich features by using syntactic information of the text. From our observation when investigating opinion evidences in the texts, we will propose syntax-based patterns which are used for extracting rich linguistic features. Combining these new features with conventional features from previous studies, we obtain a high accuracy (about 92.1%) for detecting subjective sentences on the Movie review data.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
11
Average
Average
Average
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