People's conduct and reactions are driven by their emotions. Online social media is becoming a great instrument for expressing emotions in written form. Paying attention to the context and the entire sentence help us to detect emotion from texts. However, this perspective inhibits us from noticing some emotional words or phrases in the text, particularly when the words express an emotion implicitly rather than explicitly. On the other hand, focusing only on the words and ignoring the context results in a distorted understanding of the sentence meaning and feeling. In this paper, we propose a framework that analyses text at both the sentence and word levels. We name it CEFER (Context and Emotion embedded Framework for Emotion Recognition). Our four approach facets are to extracting data by considering the entire sentence and each individual word simultaneously, as well as implicit and explicit emotions. The knowledge gained from these data not only mitigates the impact of flaws in the preceding approaches but also it strengthens the feature vector. We evaluate several feature spaces using BERT family and design the CEFER based on them. CEFER combines the emotional vector of each word, including explicit and implicit emotions, with the feature vector of each word based on context. CEFER performs better than the BERT family. The experimental results demonstrate that identifying implicit emotions are more challenging than detecting explicit emotions. CEFER, improves the accuracy of implicit emotion recognition. According to the results, CEFER perform 5% better than the BERT family in recognizing explicit emotions and 3% in implicit.
翻译:人们的行为和反应是由情感驱使的。 在线社交媒体正在成为用书面形式表达情感的伟大工具。 关注上下文和整个句子有助于我们从文本中发现情感。 但是,这种观点使我们无法注意到文本中的某些情感文字或词句, 特别是当文字暗含而不是明确地表达情感时。 另一方面, 仅仅关注文字,忽视背景结果, 从而扭曲了对句子含义和感觉的理解。 在本文中, 我们提议一个框架, 分析句子和字层的文字。 我们命名它为 CEFER(情感内嵌和情感内嵌框架) 。 我们的四种方法是通过同时考虑整个句子和每个单词以及隐含和直露的情感来提取数据。 从这些数据中获取的知识不仅减轻了先前方法中的缺陷的影响,而且加强了特性矢量。 我们利用BERT家庭对几个特征空间进行了评估, 并设计了基于这些内容的CEFER。 CEFER将每个词的情感媒介矢量(包括直言和隐含情感)与每个词的特性矢量矢量, 以CEFER 更清楚地显示C- 更明确地显示CFER 3 而不是直隐含的C- 。 更清楚的CFER, 更清楚地的情感, 更清楚地显示了CFER 而不是精确的情感的C- 改进了对C- 的情感的情感的情感的精确性结果。