A rapid growth of web documents due to heavy use of World Wide Web necessitates efficient techniques to efficiently classify the document on the web. It is thus produced High volumes of data per second with high diversity. Automatically classification of these growing amounts of web document is One of the biggest challenges facing us today. Probabilistic classification algorithms such as Naive Bayes have become commonly used for web document classification. This problem is mainly because of the irrelatively high classification accuracy on plenty application areas as well as their lack of support to handle high dimensional and sparse data which is the exclusive characteristics of textual data representation. also it is common to Lack of attention and support the semantic relation between words using traditional feature selection method When dealing with the big data and large-scale web documents. In order to solve the problem, we proposed a method for web document classification that uses LSA to increase similarity of documents under the same class and improve the classification precision. Using this approach, we designed a faster and much accurate classifier for Web Documents. Experimental results have shown that using the mentioned preprocessing can improve accuracy and speed of Naive Bayes availably, the precision and recall metrics have indicated the improvement.
Supervised learning Methods for Bangla Web Document Categorization
University Malaysia Pahang（马来西亚彭亨大学）；Hajee Mohammad Danesh Science and Technology University（哈吉·穆罕默德·达内什科学技术大学）；Rajshai University of Engineering and Technology（拉杰沙希工程技术大学）
Utilization of Multinomial Naive Bayes Algorithm and Term Frequency Inverse Document Frequency (TF-IDF Vectorizer) in Checking the Credibility of News Tweet in the Philippines
College of Engineering and Technology（工程与技术学院）；Pamantasan ng Lungsod ng Maynila（马尼拉市立大学）
Sentiment Analysis of Review Datasets Using Naive Bayes and K-NN Classifier
Heritage Institute of Technology（遗产技术学院）；Institute of Engineering & Management（工程与管理学院）
Web 2.0的出现导致了Web中可用的情感内容数量的增加。这样的内容通常以电影或产品评论、用户评论、推荐、论坛中的消息等形式出现在社交媒体网站中。及时发现情感或固执己见的网络内容具有许多优点，其中最重要的是货币化。了解大众对不同实体和产品的情感，可以为上下文广告、推荐系统和市场趋势分析提供更好的服务。我们项目的重点是情感集中的网络爬行框架，以方便快速发现电影评论和酒店评论的情感内容，并进行分析。我们使用统计方法来捕捉主观风格和句子极性的元素。本文详细讨论了两种有监督的机器学习算法：K-最近邻（K-NN）和朴素贝叶斯（Naive Bayes），并比较了它们的总体准确率、精确率和召回率。可以看出，在电影评论的情况下，朴素贝叶斯给出了比K-NN好得多的结果，但对于酒店评论，这些算法给出了较少的，几乎相同的准确性。
San Diego Supercomputer Center, University of California, San Diego（加州大学圣迭戈分校圣迭戈超级计算机中心）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；Yonsei University College of Medicine（延世大学医学院）
