Combining Likes-Retweet Analysis and Naive Bayes Classifier within Twitter for Sentiment Analysis
Combining Likes-Retweet Analysis and Naive Bayes Classifier within Twitter for Sentiment Analysis
复制标题
结合 Twitter 内的点赞转发分析和朴素贝叶斯分类器进行情感分析
DOI：
--
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Aryo Pinandito
中科院分区：
文献类型：
--
作者：
Rizal Setya Perdana;Aryo Pinandito
情感分析是一项旨在提取主观性意见的研究。由于社交媒体中用户生成内容的大量增长，Twitter是最受欢迎的微博应用程序之一，用户可以自由地讨论和分享对特定主题或实体的意见。Twitter有几个功能可以用来改进情感分析，比如点赞和转发。“喜欢”和“转发”是Twitter中传播或分享以及对其他用户发布的内容表示赞赏的机制。本文提出了一种结合文本和非文本特征的方法来提高情感预测的性能。在本研究中，我们使用朴素贝叶斯进行文本分类，并使用Fisher评分来确定非文本（喜欢和转发）特征。通过结合两种特征，我们的实验找到了α和β的最优值。采用f1测度的评价性能，准确率为0.838，α和β分别为0.6和0.4。
Sentiment analysis is a research study that aims to extract subjectivity of opinions. Due to massive growth number of user generated content in social media, Twitter is one of the most popular microblogging application which user is freely to discuss and share opinions about specific topic or entity. Twitter have several features that potentially can be used to improve sentiment analysis such as like and retweet. Like and retweet are mechanism in Twitter to propagate or share and to show appreciation of other user posting. This paper proposes a combination of textual and non-textual features to improve performance of sentiment prediction. In this research we apply Naive Bayes for textual classification and Fisher Score to determine non-textual (like and retweet) features. By combining two kinds of features, our experimental find the optimal value of α and β. The evaluation performance using F1-measure gives 0.838 of accuracy with α and β are 0.6 and 0.4 respectively.