Extraction and evaluation of popular online trends: A case of Pantip.com

2017 9th International Conference on Information Technology and Electrical Engineering (ICITEE) Pub Date : 2017-10-01 DOI:10.1109/ICITEED.2017.8250454

Bundit Thanasopon, Nattawut Sumret, Jirawin Buranapanitkij, P. Netisopakul

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引用次数: 2

Abstract

Popular online trends detection from crowd becomes more and more essential for both trend followers and online sellers. However, huge amount of online posts, both text and images, has prevented trends detection to be manually processed. This article, focusing on text mining, aims to automatically extract popular online trends. A case study is performed on one of the most popular discussion forum websites in Thailand — i.e., Pantip.com. The approach involves employing several unsupervised text mining techniques, namely, TF-IDF and HTML scores, and supervised learning sentiment classification, to extract key topics and assess sentiment results, respectively. Also, we conducted an experiment on the performance of sentiment classification and found that support vector machine (SVM) outperformed other learning techniques. In addition, the authors developed a web- application incorporating the proposed approach. The application collects data from Pantip.com, identifies key concepts of posts and calculates the popularity of each key concept based on statistics and sentiment results.

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网络流行趋势的提取与评价——以Pantip.com为例

从人群中发现网络流行趋势对于趋势追随者和网络卖家来说都变得越来越重要。然而，大量的在线帖子(包括文本和图像)阻碍了趋势检测的人工处理。本文以文本挖掘为重点，旨在自动提取流行的在线趋势。案例研究是在泰国最受欢迎的论坛网站之一Pantip.com上进行的。该方法涉及使用几种无监督文本挖掘技术，即TF-IDF和HTML分数，以及监督学习情感分类，分别提取关键主题和评估情感结果。此外，我们对情感分类的性能进行了实验，发现支持向量机(SVM)优于其他学习技术。此外，作者还开发了一个包含该方法的web应用程序。该应用程序从Pantip.com收集数据，识别帖子的关键概念，并根据统计数据和情感结果计算每个关键概念的受欢迎程度。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

2017 9th International Conference on Information Technology and Electrical Engineering (ICITEE)

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