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第59行： − + 第77行：
第77行： − 随后，火山和福格尔在一项专利中描述的方法，专门研究了情感，并根据不同的情感尺度识别了文本中的单个单词和短语。一个基于他们的工作的现行系统，称为 EffectCheck，提出了同义词，可以用来增加或减少在每个规模的诱发情绪的水平。+ 第112行：
第112行： − 随后的许多努力都没有那么复杂，仅仅使用了从正面到负面的情绪极性视角，比如特尼和彭日成分别使用了不同的方法来检测产品评论和电影评论的极性。这项工作是在文档级别进行的。人们还可以在多方面的尺度上对文件的极性进行分类，彭日成和斯奈德等人曾尝试这样做: 彭日成和李拓展了将电影评论分为正面或负面的基本任务，以3星或4星的尺度预测明星评级，而斯奈德对餐馆评论进行了深入分析，预测特定餐馆的各个方面的评级，例如食物和氛围(以五星的尺度)。+
→Types类型
情感分析的先驱包括 ''the General Inquirer''，''<ref name=":3" />'' 这为文本和心理学研究中的量化模式提供了线索，即根据对一个人的语言行为的分析来研究其心理状态。<ref name=":4" />
情感分析的先驱包括 ''the General Inquirer''，''<ref name=":3" />'' 这为文本和心理学研究中的量化模式提供了线索，即根据对一个人的语言行为的分析来研究其心理状态。<ref name=":4" />
Subsequently, the method described in a patent by Volcani and Fogel,<ref>{{cite patent
Subsequently, the method described in a patent by Volcani and Fogel,<ref name=":5">{{cite patent
| country = USA
| country = USA
| number = 7,136,877
| number = 7,136,877
}}</ref> looked specifically at sentiment and identified individual words and phrases in text with respect to different emotional scales. A current system based on their work, called EffectCheck, presents synonyms that can be used to increase or decrease the level of evoked emotion in each scale.
}}</ref> looked specifically at sentiment and identified individual words and phrases in text with respect to different emotional scales. A current system based on their work, called EffectCheck, presents synonyms that can be used to increase or decrease the level of evoked emotion in each scale.
随后， Volcani和Fogel<ref name=":5" /> 在一项专利中描述的方法专门研究了情感并根据不同的情感尺度识别了文本中的单个单词和短语。一个基于他们的研究建立的称为EffectCheck的系统则提供了同义词，可以用来增加或减少在每个尺度的诱发情绪的水平。
Many other subsequent efforts were less sophisticated, using a mere polar view of sentiment, from positive to negative, such as work by Turney,<ref name = "Turney02" /> and Pang<ref name = "PangAl02">
Many other subsequent efforts were less sophisticated, using a mere polar view of sentiment, from positive to negative, such as work by Turney,<ref name = "Turney02" /> and Pang<ref name = "PangAl02">
</ref> among others: Pang and Lee<ref name = "PangLee05" /> expanded the basic task of classifying a movie review as either positive or negative to predict star ratings on either a 3- or a 4-star scale, while Snyder<ref name = "SnyderBarzilay07" /> performed an in-depth analysis of restaurant reviews, predicting ratings for various aspects of the given restaurant, such as the food and atmosphere (on a five-star scale).
</ref> among others: Pang and Lee<ref name = "PangLee05" /> expanded the basic task of classifying a movie review as either positive or negative to predict star ratings on either a 3- or a 4-star scale, while Snyder<ref name = "SnyderBarzilay07" /> performed an in-depth analysis of restaurant reviews, predicting ratings for various aspects of the given restaurant, such as the food and atmosphere (on a five-star scale).
之后许多的研究都没有那么复杂，仅仅使用了正负两极的情感极性视角，比如Turney<ref name="Turney02" />和Pang<ref name="PangAl02" />分别使用了不同的方法来识别产品评论和电影评论的极性。这项工作是在篇章级的粒度层次进行的。人们还可以在多层次上对篇章的极性进行分类，Pang<ref name="PangLee05" />和Snyder<ref name="SnyderBarzilay07" /> 等人曾尝试这样做：Pang和Lee<ref name="PangLee05" />拓展了仅仅将电影评论分为正面或负面的基本任务，并以三星或四星的尺度预测电影的评级；而Snyder<ref name="SnyderBarzilay07" /> 对餐馆评论进行了深入分析，预测特定餐馆的各个方面的评级，例如食物和氛围(以五星的尺度)。
First steps to bringing together various approaches—learning, lexical, knowledge-based, etc.—were taken in the 2004 [[AAAI]] Spring Symposium where linguists, computer scientists, and other interested researchers first aligned interests and proposed shared tasks and benchmark data sets for the systematic computational research on affect, appeal, subjectivity, and sentiment in text.<ref>Qu, Yan, James Shanahan, and Janyce Wiebe. "Exploring attitude and affect in text: Theories and applications." In AAAI Spring Symposium) Technical report SS-04-07. AAAI Press, Menlo Park, CA. 2004.</ref>
First steps to bringing together various approaches—learning, lexical, knowledge-based, etc.—were taken in the 2004 [[AAAI]] Spring Symposium where linguists, computer scientists, and other interested researchers first aligned interests and proposed shared tasks and benchmark data sets for the systematic computational research on affect, appeal, subjectivity, and sentiment in text.<ref>Qu, Yan, James Shanahan, and Janyce Wiebe. "Exploring attitude and affect in text: Theories and applications." In AAAI Spring Symposium) Technical report SS-04-07. AAAI Press, Menlo Park, CA. 2004.</ref>