Predicting best answer in community questions based on content and sentiment analysis

Dalia Elalfy, Walaa K. Gad, R. Ismail
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引用次数: 5

Abstract

Community question answering sites are gained much popularity in the last few years because of the wide spread of the internet and the facilities that these sites offer in question asking and answering processes. Community question answering sites are here to save the asker's time and effort and make him/her ask in a natural language and get the answer also back in natural language and from experts. To achieve these goals there are many challenges. Some of these challenges are for example, many questions appear to non-experts so we need to direct the questions to experts in the question category and specifying the best answer to a given question and etc. In this paper, we propose a novel model to find the best answer by using features that are based on question and answer content, answer context and the relation between question and its answers. We conducted experiments to train classifiers using our new added features and the accuracy of the best answer prediction result was very promising.
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基于内容和情感分析预测社区问题的最佳答案
由于互联网的广泛普及以及这些网站在提问和回答过程中提供的设施,社区问答网站在过去几年中变得非常受欢迎。社区问答网站在这里节省了提问者的时间和精力,让他/她用自然语言提问,并得到自然语言和专家的答案。要实现这些目标,有许多挑战。其中一些挑战是,例如,许多问题似乎是非专家,所以我们需要将问题直接交给问题类别中的专家,并指定给定问题的最佳答案等等。在本文中,我们提出了一个新的模型,利用基于问题和答案内容、答案上下文以及问题和答案之间的关系的特征来寻找最佳答案。我们使用新添加的特征进行了训练分类器的实验,最佳答案预测结果的准确性非常有希望。
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