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情感分析的目的和挑战可以通过一些简单的例子来说明。
情感分析的目的和挑战可以通过一些简单的例子来说明。
= = = Simple cases简单案例 = =
=== Simple cases简单案例 ===
* Coronet has the best lines of all day cruisers.
* Coronet has the best lines of all day cruisers.
* 我不喜欢旧的游艇。
* 我不喜欢旧的游艇。
= = More challenging examples更具挑战性的例子 = =
=== More challenging examples更具挑战性的例子 ===
* I do not dislike cabin cruisers. ([[Negation]] handling)
* I do not dislike cabin cruisers. ([[Negation]] handling)
它指的是确定对实体的不同特征或方面表达的意见或感情，例如，手机、数码相机或银行。功能或方面是一个实体的属性或组成部分，例如，手机的屏幕，餐厅的服务，或照相机的图像质量。基于特征的情感分析的优势在于可以捕捉感兴趣对象的细微差别。不同的特征可以产生不同的情绪反应，例如，酒店可以有一个方便的地点，但平庸的食物。这个问题涉及几个子问题，例如，识别相关实体，提取它们的特征/方面，以及确定对每个特征/方面表达的意见是积极的、消极的还是中性的。特征的自动识别可以通过句法方法、主题建模或者深度学习来实现。关于这一层次的情感分析的更详细的讨论可以在刘的作品中找到。
它指的是确定对实体的不同特征或方面表达的意见或感情，例如，手机、数码相机或银行。功能或方面是一个实体的属性或组成部分，例如，手机的屏幕，餐厅的服务，或照相机的图像质量。基于特征的情感分析的优势在于可以捕捉感兴趣对象的细微差别。不同的特征可以产生不同的情绪反应，例如，酒店可以有一个方便的地点，但平庸的食物。这个问题涉及几个子问题，例如，识别相关实体，提取它们的特征/方面，以及确定对每个特征/方面表达的意见是积极的、消极的还是中性的。特征的自动识别可以通过句法方法、主题建模或者深度学习来实现。关于这一层次的情感分析的更详细的讨论可以在刘的作品中找到。
= Methods and features方法和特征 =
== Methods and features方法和特征 ==
Existing approaches to sentiment analysis can be grouped into three main categories: knowledge-based techniques, statistical methods, and hybrid approaches.<ref name ="“Cambria">
Existing approaches to sentiment analysis can be grouped into three main categories: knowledge-based techniques, statistical methods, and hybrid approaches.<ref name="“Cambria">
{{cite journal
{{cite journal
| first1 = E
| first1 = E
|archive-date = 2015-11-23
|archive-date = 2015-11-23
}}
}}
</ref> Some knowledge bases not only list obvious affect words, but also assign arbitrary words a probable "affinity" to particular emotions.<ref name ="Stevenson">
</ref> Some knowledge bases not only list obvious affect words, but also assign arbitrary words a probable "affinity" to particular emotions.<ref name="Stevenson">
{{cite journal
{{cite journal
| first1 = Ryan
| first1 = Ryan
| doi-access = free
| doi-access = free
}}
}}
</ref> Statistical methods leverage elements from [[machine learning]] such as [[latent semantic analysis]], [[support vector machines]], "[[bag of words]]", "[[Pointwise Mutual Information]]" for Semantic Orientation,<ref name = "Turney02">
</ref> Statistical methods leverage elements from [[machine learning]] such as [[latent semantic analysis]], [[support vector machines]], "[[bag of words]]", "[[Pointwise Mutual Information]]" for Semantic Orientation,<ref name="Turney02">
{{cite conference
{{cite conference
| first = Peter | last = Turney
| first = Peter | last = Turney
}}
}}
</ref>
</ref>
在情感分析中需要一个人工分析组件，因为自动化系统不能分析个人评论者或平台的历史趋势，而且在他们表达的情感中常常被错误地分类。自动化影响了大约23% 被人类正确分类的评论。然而，人们往往不同意，并认为人际协议提供了一个上限，自动情绪分类器最终可以达到。
在情感分析中需要一个人工分析组件，因为自动化系统不能分析个人评论者或平台的历史趋势，而且在他们表达的情感中常常被错误地分类。自动化影响了大约23% 被人类正确分类的评论。然而，人们往往不同意，并认为人际协议提供了一个上限，自动情绪分类器最终可以达到。
= Evaluation 评估 =
== Evaluation 评估 ==
The accuracy of a sentiment analysis system is, in principle, how well it agrees with human judgments. This is usually measured by variant measures based on [[precision and recall]] over the two target categories of negative and positive texts. However, according to research human raters typically only agree about 80%<ref>
The accuracy of a sentiment analysis system is, in principle, how well it agrees with human judgments. This is usually measured by variant measures based on [[precision and recall]] over the two target categories of negative and positive texts. However, according to research human raters typically only agree about 80%<ref>
}}
}}
</ref>
</ref>