SAFE: A Sentiment Analysis Framework for E-Learning
SAFE: A Sentiment Analysis Framework for E-Learning
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DOI：
10.3991/ijet.v9i6.4110
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发表时间：
2014-01-01
影响因子：
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通讯作者：
Greco, Luca
中科院分区：
文献类型：
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作者：
Colace, Francesco;De Santo, Massimo;Greco, Luca
社交网络的普及使得人们可以在生活的不同方面分享意见，每天有数百万条信息出现在网络上。这些文本信息可以成为意见挖掘和情感分析的丰富数据来源：对文本中表达的意见，情感和情绪的计算研究。它的主要目的是在评论或评论中识别处理积极或消极情绪的同意或不同意陈述。在本文中，我们调查通过，在电子学习领域，基于潜在狄利克雷分配（LDA）的概率方法作为情感抓取器。通过这种方法，对于属于同一知识领域的一组文档，可以自动提取一个图，即术语混合图。本文展示了这个图如何包含一组加权的词对，这是歧视性的情感分类。通过这种方式，系统可以检测学生对某些主题的感受，教师可以更好地调整他/她的教学方法。事实上，所提出的方法已经在来自电子学习平台的数据集上进行了测试。初步的实验表明，该方法是有效的和令人满意的。
The spread of social networks allows sharing opinions on different aspects of life and daily millions of messages appear on the web. This textual information can be a rich source of data for opinion mining and sentiment analysis: the computational study of opinions, sentiments and emotions expressed in a text. Its main aim is the identif cation of the agreement or disagreement statements that deal with positive or negative feelings in comments or reviews. In this paper, we investigate the adoption, in the field of the e-learning, of a probabilistic approach based on the Latent Dirichlet Allocation (LDA) as Sentiment grabber. By this approach, for a set of documents belonging to a same knowledge domain, a graph, the Mixed Graph of Terms, can be automatically extracted. The paper shows how this graph contains a set of weighted word pairs, which are discriminative for sentiment classication. In this way, the system can detect the feeling of students on some topics and teacher can better tune his/ her teaching approach. In fact, the proposed method has been tested on datasets coming from e-learning platforms. A preliminary experimental campaign shows how the proposed approach is effective and satisfactory.