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→Subjectivity/objectivity identification 主观性/客观性识别
The term objective refers to the incident carry factual information.<ref name="Wiebe 2005 486–497">{{Cite journal|last1=Wiebe|first1=Janyce|last2=Riloff|first2=Ellen|date=2005|editor-last=Gelbukh|editor-first=Alexander|title=Creating Subjective and Objective Sentence Classifiers from Unannotated Texts|url=https://link.springer.com/chapter/10.1007%2F978-3-540-30586-6_53|journal=Computational Linguistics and Intelligent Text Processing|series=Lecture Notes in Computer Science|volume=3406|language=en|location=Berlin, Heidelberg|publisher=Springer|pages=486–497|doi=10.1007/978-3-540-30586-6_53|isbn=978-3-540-30586-6}}</ref>
The term objective refers to the incident carry factual information.<ref name="Wiebe 2005 486–497">{{Cite journal|last1=Wiebe|first1=Janyce|last2=Riloff|first2=Ellen|date=2005|editor-last=Gelbukh|editor-first=Alexander|title=Creating Subjective and Objective Sentence Classifiers from Unannotated Texts|url=https://link.springer.com/chapter/10.1007%2F978-3-540-30586-6_53|journal=Computational Linguistics and Intelligent Text Processing|series=Lecture Notes in Computer Science|volume=3406|language=en|location=Berlin, Heidelberg|publisher=Springer|pages=486–497|doi=10.1007/978-3-540-30586-6_53|isbn=978-3-540-30586-6}}</ref>
客观指的是具有事实信息的事件。<ref name="Wiebe 2005 486–497" />
* Example of an objective sentence: 'To be elected president of the United States, a candidate must be at least thirty-five years of age.'
* Example of an objective sentence: 'To be elected president of the United States, a candidate must be at least thirty-five years of age.'
* 客观句的例子:”要当选美国总统，候选人必须年满35岁。'
* 客观句的例子：“要当选美国总统，候选人必须年满35岁。”
The term subjective describes the incident contains non-factual information in various forms, such as personal opinions, judgment, and predictions. Also known as 'private states' mentioned by Quirk et al.<ref name=":10">{{Cite book|last1=Quirk|first1=Randolph|title=A Comprehensive Grammar of the English Language (General Grammar)|last2=Greenbaum|first2=Sidney|last3=Geoffrey|first3=Leech|last4=Jan|first4=Svartvik|publisher=[[Longman]]|year=1985|isbn=1933108312|pages=175–239}}</ref> In the example down below, it reflects a private states 'We Americans'. Moreover, the target entity commented by the opinions can take several forms from tangible product to intangible topic matters stated in Liu(2010).<ref name="Liu2010" /> Furthermore, three types of attitudes were observed by Liu(2010), 1) positive opinions, 2) neutral opinions, and 3)negative opinions.<ref name="Liu2010" />
主观这个术语描述的事件包含各种形式的非事实信息，如个人意见、判断和预测。也被Quirk等人称为“私人状况（private states）”。<ref name=":10" /> 在下面的例子中，它反映了“我们美国人”这样一个私人状态。此外，被评论的目标实体可以是从有形到无形的话题事项等多种形式（Liu,2010）。<ref name="Liu2010" /> 此外，刘（2010）还观察到三种类型的态度: 1)积极的观点，2)中性的观点，3)消极的观点。<ref name="Liu2010" />
* Example of a subjective sentence: 'We Americans need to elect a president who is mature and who is able to make wise decisions.'
* Example of a subjective sentence: 'We Americans need to elect a president who is mature and who is able to make wise decisions.'
This analysis is a classification problem.<ref name=":1" />
This analysis is a classification problem.<ref name=":1" />
* 主观句的例子: 我们美国人需要选出一位成熟且能够做出明智决定的总统。'这种分析是一个分类问题。
* 主观句的例子：“我们美国人需要选出一位成熟且能够做出明智决定的总统。”
这种分析是一个分类的问题。<ref name=":1" />
Each class's collections of words or phrase indicators are defined for to locate desirable patterns on unannotated text. For subjective expression, a different word list has been created. Lists of subjective indicators in words or phrases have been developed by multiple researchers in the linguist and natural language processing field states in Riloff et al.(2003).<ref>{{Cite journal|last1=Riloff|first1=Ellen|last2=Wiebe|first2=Janyce|date=2003-07-11|title=Learning extraction patterns for subjective expressions|journal=Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing|series=EMNLP '03|volume=10|location=USA|publisher=Association for Computational Linguistics|pages=105–112|doi=10.3115/1119355.1119369|s2cid=6541910|doi-access=free}}</ref> A dictionary of extraction rules has to be created for measuring given expressions. Over the years, in subjective detection, the features extraction progression from curating features by hands in 1999 to automated features learning in 2005.<ref>{{Cite journal|last1=Chaturvedi|first1=Iti|last2=Cambria|first2=Erik|last3=Welsch|first3=Roy E.|last4=Herrera|first4=Francisco|date=November 2018|title=Distinguishing between facts and opinions for sentiment analysis: Survey and challenges|url=https://sentic.net/subjectivity-detection.pdf|journal=Information Fusion|volume=44|pages=65–77|doi=10.1016/j.inffus.2017.12.006|via=Elsevier Science Direct|doi-access=free}}</ref> At the moment, automated learning methods can further separate into supervised and [[Unsupervised learning|unsupervised machine learning]]. Patterns extraction with machine learning process annotated and unannotated text have been explored extensively by academic researchers.
Each class's collections of words or phrase indicators are defined for to locate desirable patterns on unannotated text. For subjective expression, a different word list has been created. Lists of subjective indicators in words or phrases have been developed by multiple researchers in the linguist and natural language processing field states in Riloff et al.(2003).<ref>{{Cite journal|last1=Riloff|first1=Ellen|last2=Wiebe|first2=Janyce|date=2003-07-11|title=Learning extraction patterns for subjective expressions|journal=Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing|series=EMNLP '03|volume=10|location=USA|publisher=Association for Computational Linguistics|pages=105–112|doi=10.3115/1119355.1119369|s2cid=6541910|doi-access=free}}</ref> A dictionary of extraction rules has to be created for measuring given expressions. Over the years, in subjective detection, the features extraction progression from curating features by hands in 1999 to automated features learning in 2005.<ref>{{Cite journal|last1=Chaturvedi|first1=Iti|last2=Cambria|first2=Erik|last3=Welsch|first3=Roy E.|last4=Herrera|first4=Francisco|date=November 2018|title=Distinguishing between facts and opinions for sentiment analysis: Survey and challenges|url=https://sentic.net/subjectivity-detection.pdf|journal=Information Fusion|volume=44|pages=65–77|doi=10.1016/j.inffus.2017.12.006|via=Elsevier Science Direct|doi-access=free}}</ref> At the moment, automated learning methods can further separate into supervised and [[Unsupervised learning|unsupervised machine learning]]. Patterns extraction with machine learning process annotated and unannotated text have been explored extensively by academic researchers.