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公开(公告)号：US07987188B2
公开(公告)日：2011-07-26
申请号：US11844222
申请日：2007-08-23
IPC分类号： G06F17/30
CPC分类号： G06F17/30616
摘要： A domain-specific sentiment classifier that can be used to score the polarity and magnitude of sentiment expressed by domain-specific documents is created. A domain-independent sentiment lexicon is established and a classifier uses the lexicon to score sentiment of domain-specific documents. Sets of high-sentiment documents having positive and negative polarities are identified. The n-grams within the high-sentiment documents are filtered to remove extremely common n-grams. The filtered n-grams are saved as a domain-specific sentiment lexicon and are used as features in a model. The model is trained using a set of training documents which may be manually or automatically labeled as to their overall sentiment to produce sentiment scores for the n-grams in the domain-specific sentiment lexicon. This lexicon is used by the domain-specific sentiment classifier.
摘要翻译： 创建一个域特定情绪分类器，可用于评估由领域特定文档表达的情绪的极性和程度。 建立一个独立于领域的情绪词典，一个分类器使用词典来评价领域特定文件的情感。 确定了具有正极性和负极性的高情绪文件。 高信度文件中的n-gram被过滤以去除非常常见的n-gram。 过滤的n-gram被保存为域特定的情绪词典，并被用作模型中的特征。 该模型使用一组培训文件进行培训，培训文档可以手动或自动标记为对整个情境的整体情绪，以便在域特定情绪词典中产生n-gram的情绪评分。 该词典由域特定情绪分类器使用。
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公开(公告)号：US20090125371A1
公开(公告)日：2009-05-14
申请号：US11844222
申请日：2007-08-23
IPC分类号： G06F17/30
CPC分类号： G06F17/30616
摘要： A domain-specific sentiment classifier that can be used to score the polarity and magnitude of sentiment expressed by domain-specific documents is created. A domain-independent sentiment lexicon is established and a classifier uses the lexicon to score sentiment of domain-specific documents. Sets of high-sentiment documents having positive and negative polarities are identified. The n-grams within the high-sentiment documents are filtered to remove extremely common n-grams. The filtered n-grams are saved as a domain-specific sentiment lexicon and are used as features in a model. The model is trained using a set of training documents which may be manually or automatically labeled as to their overall sentiment to produce sentiment scores for the n-grams in the domain-specific sentiment lexicon. This lexicon is used by the domain-specific sentiment classifier.
摘要翻译： 创建一个域特定情绪分类器，可用于评估由领域特定文档表达的情绪的极性和程度。 建立一个独立于领域的情绪词典，一个分类器使用词典来评价领域特定文件的情感。 确定了具有正极性和负极性的高情绪文件。 高信度文件中的n-gram被过滤以去除非常常见的n-gram。 过滤的n-gram被保存为域特定的情绪词典，并被用作模型中的特征。 该模型使用一组培训文件进行培训，培训文档可以手动或自动标记为对整个情境的整体情绪，以便在域特定情绪词典中产生n-gram的情绪评分。 该词典由域特定情绪分类器使用。
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公开(公告)号：US08799773B2
公开(公告)日：2014-08-05
申请号：US12051798
申请日：2008-03-19
CPC分类号： G06N5/025 , G06F17/30719
摘要： Phrases in the reviews that express sentiment about a particular aspect are identified. Reviewable aspects of the entity are also identified. The reviewable aspects include static aspects that are specific to particular types of entities and dynamic aspects that are extracted from the reviews of a specific entity instance. The sentiment phrases are associated with the reviewable aspects to which the phrases pertain. The sentiment expressed by the phrases associated with each aspect is summarized, thereby producing a summary of sentiment associated with each reviewable aspect of the entity. The summarized sentiment and associated phrases can be stored and displayed to a user as a summary description of the entity.
摘要翻译： 确定了对特定方面表达情感的评论中的短语。 还确定了实体的可审查方面。 可审查的方面包括特定类型的实体的静态方面和从特定实体实例的审查中提取的动态方面。 情绪短语与短语所涉及的可审查方面相关联。 总结了与每个方面相关的短语表达的情绪，从而产生与实体的每个可审查方面相关的情绪的总结。 总结情绪和相关短语可以作为实体的简要描述存储和显示给用户。
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公开(公告)号：US08356030B2
公开(公告)日：2013-01-15
申请号：US13163623
申请日：2011-06-17
IPC分类号： G06F17/30
CPC分类号： G06F17/30616
摘要： A domain-specific sentiment classifier that can be used to score the polarity and magnitude of sentiment expressed by domain-specific documents is created. A domain-independent sentiment lexicon is established and a classifier uses the lexicon to score sentiment of domain-specific documents. Sets of high-sentiment documents having positive and negative polarities are identified. The n-grams within the high-sentiment documents are filtered to remove extremely common n-grams. The filtered n-grams are saved as a domain-specific sentiment lexicon and are used as features in a model. The model is trained using a set of training documents which may be manually or automatically labeled as to their overall sentiment to produce sentiment scores for the n-grams in the domain-specific sentiment lexicon. This lexicon is used by the domain-specific sentiment classifier.
摘要翻译： 创建一个域特定情绪分类器，可用于评估由领域特定文档表达的情绪的极性和程度。 建立一个独立于领域的情绪词典，一个分类器使用词典来评价领域特定文件的情感。 确定了具有正极性和负极性的高情绪文件。 高信度文件中的n-gram被过滤以去除非常常见的n-gram。 过滤的n-gram被保存为域特定的情绪词典，并被用作模型中的特征。 该模型使用一组培训文件进行培训，培训文档可以手动或自动标记为对整个情境的整体情绪，以便在域特定情绪词典中产生n-gram的情绪评分。 该词典由域特定的情感分类器使用。
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公开(公告)号：US20110252036A1
公开(公告)日：2011-10-13
申请号：US13163623
申请日：2011-06-17
IPC分类号： G06F17/30
CPC分类号： G06F17/30616
摘要： A domain-specific sentiment classifier that can be used to score the polarity and magnitude of sentiment expressed by domain-specific documents is created. A domain-independent sentiment lexicon is established and a classifier uses the lexicon to score sentiment of domain-specific documents. Sets of high-sentiment documents having positive and negative polarities are identified. The n-grams within the high-sentiment documents are filtered to remove extremely common n-grams. The filtered n-grams are saved as a domain-specific sentiment lexicon and are used as features in a model. The model is trained using a set of training documents which may be manually or automatically labeled as to their overall sentiment to produce sentiment scores for the n-grams in the domain-specific sentiment lexicon. This lexicon is used by the domain-specific sentiment classifier.
摘要翻译： 创建一个域特定情绪分类器，可用于评估由领域特定文档表达的情绪的极性和程度。 建立一个独立于领域的情绪词典，一个分类器使用词典来评价领域特定文件的情感。 确定了具有正极性和负极性的高情绪文件。 高信度文件中的n-gram被过滤以去除非常常见的n-gram。 过滤的n-gram被保存为域特定的情绪词典，并被用作模型中的特征。 该模型使用一组培训文件进行培训，培训文档可以手动或自动标记为对整个情境的整体情绪，以便在域特定情绪词典中产生n-gram的情绪评分。 该词典由域特定情绪分类器使用。
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公开(公告)号：US20090193328A1
公开(公告)日：2009-07-30
申请号：US12051798
申请日：2008-03-19
IPC分类号： G06F17/27
CPC分类号： G06N5/025 , G06F17/30719
摘要： Reviews express sentiment about one or more entities. Phrases in the reviews that express sentiment about a particular aspect are identified. Reviewable aspects of the entity are also identified. The reviewable aspects include static aspects that are specific to particular types of entities and dynamic aspects that are extracted from the reviews of a specific entity instance. The sentiment phrases are associated with the reviewable aspects to which the phrases pertain. The sentiment expressed by the phrases associated with each aspect is summarized, thereby producing a summary of sentiment associated with each reviewable aspect of the entity. The summarized sentiment and associated phrases can be stored and displayed to a user as a summary description of the entity.
摘要翻译： 评论一个或多个实体的表达情绪。 确定了对特定方面表达情感的评论中的短语。 还确定了实体的可审查方面。 可审查的方面包括特定类型的实体的静态方面和从特定实体实例的审查中提取的动态方面。 情绪短语与短语所涉及的可审查方面相关联。 总结了与每个方面相关的短语表达的情绪，从而产生与实体的每个可审查方面相关的情绪的总结。 总结情绪和相关短语可以作为实体的简要描述存储和显示给用户。
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公开(公告)号：US08402036B2
公开(公告)日：2013-03-19
申请号：US13167695
申请日：2011-06-24
IPC分类号： G06F17/30
CPC分类号： G06F17/30719 , Y10S707/99933 , Y10S707/99935
摘要： Disclosed herein is a method, a system and a computer product for generating a snippet for an entity, wherein each snippet comprises a plurality of sentiments about the entity. One or more textual reviews associated with the entity is selected. A plurality of sentiment phrases are identified based on the one or more textual reviews, wherein each sentiment phrase comprises a sentiment about the entity. One or more sentiment phrases from the plurality of sentiment phrases are selected to generate a snippet.
摘要翻译： 本文公开了一种用于为实体生成代码片段的方法，系统和计算机产品，其中每个片段包括关于该实体的多个情绪。 选择与该实体相关联的一个或多个文本评论。 基于一个或多个文本评论来识别多个情绪短语，其中每个情绪短语包括关于该实体的情绪。 选择来自多个情绪短语的一个或多个情绪短语以生成片段。
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