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引用次数: 0

摘要

在以电脑为媒介的交流中,尤其是在聊天和社交媒体中,表情符号是表达观点和态度等情感的重要手段。为了有效地捕捉这些情绪,必须知道与所使用的表情符号相关的情绪。以前确定表情符号所表达的情感的方法需要大量的手工注释。对于许多表情符号,特别是使用频率较低的平台专用表情符号,目前还没有对所表达的情绪进行研究。因此,到目前为止,这些表情符号还不能用于情感分析。在这项工作中,开发了一种有效和高效地确定表情符号情感并将其汇编到情感词典中的方法。将已确定的情绪编成情感词典。为此,用Python创建了软件来将文本集合处理成语料库。该软件根据出现表情符号的文本的情感,推导出表情符号的情感作为价值。该方法生成的词汇可用于基于词汇的情感分析方法。该方法还可以提取有关表情符号及其使用的其他信息,这些信息可用于评估表情符号产生的情感词典和使用情况。利用所开发的方法,对不同文本来源的语料库进行了两次分析。结果以及随后与现有情感词典的比较表明,所开发的方法能够有效地产生与手动注释产生的情感词典相似的结果。
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Design and Development of an Emoji Sentiment Lexicon
Emojis represent an essential means of expressing sentiments such as opinions and attitudes in computer - mediated communication, especially in chats and social media. To effectively capture these sentiments, the sentiments associated with the emojis used must be known. Previous approaches to determining the sentiments expressed with emojis require a large amount of manual annotation. For many emojis, especially less frequently used platform - specific emojis, studies on expressed sentiments do not yet exist. Therefore, these emojis cannot be considered in sentiment analyses so far. In this work, a method for effective and efficient determination of emojis’ sentiments and their compilation in a sentiment lexicon was developed. The determined sentiments are compiled as a sentiment lexicon. For this purpose, software was created in Python to process collections of texts into a corpus. The software derives the emojis’ sentiments as valence values based on the sentiments of the texts in which the emojis appear. The lexicons produced by the method can be used in lexicon - based sentiment analysis approaches. The method also derives other information on the emojis and their usage that can be used to assess the sentiment lexicon produced and the usage of the emojis. Using the developed method, two analyses were conducted with corpora of different text sources. The results and subsequent comparisons with existing sentiment lexicons have shown that the developed method is able to efficiently produce similar results as sentiment lexicons produced with manual annotation.
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