In machine learning,sentiment analysis is a technique to find and analyze the sentiments hidden in the text.For sentiment analysis,annotated data is a basic requirement.Generally,this data is manually annotated.Manual...In machine learning,sentiment analysis is a technique to find and analyze the sentiments hidden in the text.For sentiment analysis,annotated data is a basic requirement.Generally,this data is manually annotated.Manual annotation is time consuming,costly and laborious process.To overcome these resource constraints this research has proposed a fully automated annotation technique for aspect level sentiment analysis.Dataset is created from the reviews of ten most popular songs on YouTube.Reviews of five aspects—voice,video,music,lyrics and song,are extracted.An N-Gram based technique is proposed.Complete dataset consists of 369436 reviews that took 173.53 s to annotate using the proposed technique while this dataset might have taken approximately 2.07 million seconds(575 h)if it was annotated manually.For the validation of the proposed technique,a sub-dataset—Voice,is annotated manually as well as with the proposed technique.Cohen’s Kappa statistics is used to evaluate the degree of agreement between the two annotations.The high Kappa value(i.e.,0.9571%)shows the high level of agreement between the two.This validates that the quality of annotation of the proposed technique is as good as manual annotation even with far less computational cost.This research also contributes in consolidating the guidelines for the manual annotation process.展开更多
Boundary recognition is an important research of natural language processing, and it provides a basis for the application of Chinese word segmentation, chunk analysis, named entity recognition, etc. Based on ambiguity...Boundary recognition is an important research of natural language processing, and it provides a basis for the application of Chinese word segmentation, chunk analysis, named entity recognition, etc. Based on ambiguity in boundary recognition of Chinese punctuation marks, this paper proposes grammar testing methods for boundary recognition of slight-pause marks and then calculates the annotation consistency of these methods. The statistical results show that grammar testing methods can greatly improve the annotation consistency of slight-pause marks boundary recognition. The consistency during the second time is 0.030 3 higher than during the first, which will help guarantee the consistency of large-scale corpus annotation and improve the quality of corpus annotation.展开更多
文摘In machine learning,sentiment analysis is a technique to find and analyze the sentiments hidden in the text.For sentiment analysis,annotated data is a basic requirement.Generally,this data is manually annotated.Manual annotation is time consuming,costly and laborious process.To overcome these resource constraints this research has proposed a fully automated annotation technique for aspect level sentiment analysis.Dataset is created from the reviews of ten most popular songs on YouTube.Reviews of five aspects—voice,video,music,lyrics and song,are extracted.An N-Gram based technique is proposed.Complete dataset consists of 369436 reviews that took 173.53 s to annotate using the proposed technique while this dataset might have taken approximately 2.07 million seconds(575 h)if it was annotated manually.For the validation of the proposed technique,a sub-dataset—Voice,is annotated manually as well as with the proposed technique.Cohen’s Kappa statistics is used to evaluate the degree of agreement between the two annotations.The high Kappa value(i.e.,0.9571%)shows the high level of agreement between the two.This validates that the quality of annotation of the proposed technique is as good as manual annotation even with far less computational cost.This research also contributes in consolidating the guidelines for the manual annotation process.
基金Supported by the National Natural Science Foundation of China(61373108)Humanities and Social Science Foundation of Ministry of Education of China(16YJCZH004)the Major Projects of the National Social Science Foundation of China(11&ZD189)
文摘Boundary recognition is an important research of natural language processing, and it provides a basis for the application of Chinese word segmentation, chunk analysis, named entity recognition, etc. Based on ambiguity in boundary recognition of Chinese punctuation marks, this paper proposes grammar testing methods for boundary recognition of slight-pause marks and then calculates the annotation consistency of these methods. The statistical results show that grammar testing methods can greatly improve the annotation consistency of slight-pause marks boundary recognition. The consistency during the second time is 0.030 3 higher than during the first, which will help guarantee the consistency of large-scale corpus annotation and improve the quality of corpus annotation.
