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Taxonomy of keyword extraction in Facebook using Decision Tree algorithm in NLP

机译:在Facebook中使用决策树算法在NLP中使用决策树算法的关键字提取分类

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The main idea of our project is to extract keywords from the collection of dataset from Facebook account data like comment, post by the people. Then, By extracting the keywords from the specific account, we can provide the advertisement with help of the business organizations, to improve the business growth of each organization. Text can be an extremely valuable source of information, but extracting insights from the data can be hard and time-consuming due to its unstructured nature. Businesses are performing to text classification for structuring text in a fast and cost-efficient way to enhance decision-making and automate processes in the model. Instead of relying on manually crafted rules, text classification in machine learning learns to make classifications based on past observations. By using pre-labelled examples as training data, a machine learning algorithm can learn the different subset between pieces of text and that a particular output is expected for a particular input.
机译:我们项目的主要思想是从数据集的集合中提取关键字,从Facebook帐户数据等评论,由人员发布。然后,通过从特定账户中提取关键字,我们可以在业务组织的帮助下提供广告,以提高每个组织的业务增长。文本可以是一个非常有价值的信息来源,但由于其非结构化性质,提取数据的见解可能是艰难而耗时的。企业正在以快速且经济高效的方式对构建文本进行文本分类,以增强模型中的决策和自动化进程。而不是依靠手动制作的规则,机器学习中的文本分类学会基于过去的观察来进行分类。通过使用预先标记的示例作为训练数据,机器学习算法可以在文本片段之间学习不同的子集,并且需要针对特定​​输入的特定输出。

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