推文情感分类中的Emoji和Emoticon

A. A. Arifiyanti, E. D. Wahyuni
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引用次数: 5

摘要

印尼的推特用户在发推特时倾向于使用表情符号或表情符号。表情符号和表情符号可以帮助他们有效地传达信息。这是用来表达他们对某个话题的看法。用分类技术分析情感,可以考虑使用表情符号和表情符号。在本文中,我们知道印尼Twitter的用户在推特中不仅使用emoji,还使用emoticon,虽然使用的是emoji而不是emoticon。在某些情况下,两者在tweet中同时使用。表情符号和表情符号对分类模型的性能有积极的影响,使得表情符号和表情符号可能无法去除文本预处理并包含在分类特征中。对于包含在分类特征中的表情符号和表情符号转换为其Unicode名称,但这种转换略低于分类模型从转换表情符号和表情符号到其词汇的情感。将表情符号和表情符号转换为其词汇情感,使分类模型性能更加稳定。但考虑到表情符号和表情符号不能简单地解释为某种情感类别,因此转换为Unicode名称是令人鼓舞的。SVM的性能最好,其次是伯努利Naïve贝叶斯,最后是多项式Naïve贝叶斯。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Emoji and Emoticon in Tweet Sentiment Classification
Indonesian Twitter’s user tends to use emoji or emoticon when tweeting. Emoji and emoticon can help them for conveying their message efficiently. It is used for expressing their sentiment about a certain topic. For analyzing sentiment with classification technique, the use of emoji and emoticon might take into consideration. In this paper, we knew that Indonesian Twitter’s User not only uses emoji in their tweets but also emoticon although emoji used is dominating tweets than emoticon. In some cases, both of them are used simultaneously in a tweet. Emoji and emoticon affect classification model performance positively so that emoji and emoticon might not remove in-text pre-processed and include in classification features. For included in classification feature emoji and emoticon convert into its Unicode Name, but this conversion slightly below classification model from conversion emoji and emoticon into its lexicon sentiment. Conversion emoji and emoticon into its lexicon sentiment make classification model performance more stable. But considering emoticon and emoji cannot simply be interpreted into certain sentiment class so conversion into Unicode Name is encouraging. SVM produces the best performance followed by Bernoulli Naïve Bayes and the last one is Multinomial Naïve Bayes.
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