توضیحات
ABSTRACT
In this work, we attempt to detect sentencelevel subjectivity by means of two supervise machine learning approaches: a Fuzzy Control System and Adaptive Neuro-Fuzzy Inference System. Even though these methods are popular in pattern recognition, they have not been thoroughly investigated for subjectivity analysis. We present a novel “Pruned ICF Weighting Coefficient,” which improves the accuracy for subjectivity detection. Our feature
extraction algorithm calculates a feature vector based on the statistical occurrences of words in a corpus without any lexical knowledge. For this reason, these machine learning models can be applied to any language; i.e., there is no lexical, grammatical, syntactical analysis used in the classification process
INTRODUCTION
There has been a growing interest, in recent years, in identifying and extracting subjective information from Web documents that contain opinions. Opinions are usually subjective expressions that describe people’s sentiments, appraisals, or feelings. Subjectivity detection seeks to identify whether the given text expresses opinions (subjective)
or reports facts (objective) (Lin et al., 2011). Automatic subjectivity analysis methods have been used in a wide variety of text processing and natural language applications. In many natural language processing tasks, subjectivity detection has been used as a first phase of filtering to generate more informative data
Year : 2013
Publisher : Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media
By : Samir Rustamov ,Mark A. Clements
File Information : English Language / 7 Page / Size : 75.1 K
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سال : 2013
ناشر : Samir Rustamov ,Mark A. Clements
کاری از : Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media
اطلاعات فایل : زبان انگلیسی / 7 صفحه / حجم : 75.1 K
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