Affect Analysis Model: novel rule-based approach to affect sensing from text
Affect Analysis Model: novel rule-based approach to affect sensing from text
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DOI：
10.1017/s1351324910000239
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发表时间：
2010-09
影响因子：
2.5
通讯作者：
Alena Neviarouskaya;H. Prendinger;M. Ishizuka
中科院分区：
文献类型：
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作者：
Alena Neviarouskaya;H. Prendinger;M. Ishizuka
摘要在本文中，我们讨论了在线交流环境中通过短信传达的情感的识别和解释任务。具体地说，我们关注的是即时消息(IM)或博客，在这些地方，人们使用非正式或乱码的写作风格。我们提出了一种新的基于规则的语言方法来识别文本中的情感。我们的情感分析模型(AAM)不仅被设计来处理语法和句法上正确的文本输入，而且还被设计来处理以缩写或表达方式编写的非正式消息。这种基于规则的方法对每个句子进行分阶段的处理，包括符号线索处理、缩略语的检测和转换、句子分析和词/短语/句子级别的分析。我们的方法能够处理不同复杂性的句子，包括简单句、复合句、复杂句(带补语从句和关系句)和复杂复句。文本中的情感可分为九种情感类别(或中性情感)。由此产生的情绪状态的强度取决于情绪词的载体、它们之间的关系、被分析句子的时态和第一人称代词的可用性。对情感分析模型算法的评估表明，该算法能够准确识别日记般的博客帖子(平均准确率高达77%)、童话故事(平均准确率高达70.2%)和新闻标题(我们的算法在几个指标上优于其他八个系统)中反映的细粒度情感。
Abstract In this paper, we address the tasks of recognition and interpretation of affect communicated through text messaging in online communication environments. Specifically, we focus on Instant Messaging (IM) or blogs, where people use an informal or garbled style of writing. We introduced a novel rule-based linguistic approach for affect recognition from text. Our Affect Analysis Model (AAM) was designed to deal with not only grammatically and syntactically correct textual input, but also informal messages written in an abbreviated or expressive manner. The proposed rule-based approach processes each sentence in stages, including symbolic cue processing, detection and transformation of abbreviations, sentence parsing and word/phrase/sentence-level analyses. Our method is capable of processing sentences of different complexity, including simple, compound, complex (with complement and relative clauses) and complex–compound sentences. Affect in text is classified into nine emotion categories (or neutral). The strength of the resulting emotional state depends on vectors of emotional words, relations among them, tense of the analysed sentence and availability of first person pronouns. The evaluation of the Affect Analysis Model algorithm showed promising results regarding its capability to accurately recognize fine-grained emotions reflected in sentences from diary-like blog posts (averaged accuracy is up to 77 per cent), fairy tales (averaged accuracy is up to 70.2 per cent) and news headlines (our algorithm outperformed eight other systems on several measures).