Abstract
The most of the people have their account on social networks (e.g. Facebook, Vkontakte) where they express their attitude to different situations and events. Facebook provides only the positive mark as a like button and share. However, it is important to know the position of a certain user on posts even though the opinion is negative. Positive, negative and neutral attitude can be extracted from the comments of users. Overall information about positive, negative and neutral opinion can bring the understanding of how people react in a position. Moreover, it is important to know how attitude is changing during the time period. The contribution of the paper is a new method based on sentiment text analysis for detection and prediction negative and positive patterns for Facebook comments which combines (i) real-time sentiment text analysis for pattern discovery and (ii) batch data processing for creating opinion forecasting algorithm. To perform forecast we propose two-steps algorithm where: (i) patterns are clustered using unsupervised clustering techniques and (ii) trend prediction is performed based on finding the nearest pattern from the certain cluster. Case studies show the efficiency and accuracy (Avg. MAE = 0.008) of the proposed method and its practical applicability. Also, we discovered three types of users attitude patterns and described them.
Abstract (translated)
大多数人在社交网络（如Facebook、Vkontakte）上都有自己的账户，在那里他们可以表达对不同情况和事件的态度。Facebook只提供了类似按钮和分享的积极标志。然而，了解某个用户在帖子上的位置是很重要的，即使其意见是负面的。从用户的评论中可以提炼出积极、消极、中立的态度。关于积极、消极和中立意见的总体信息可以使人们了解在一个立场上的反应。此外，了解在这段时间内态度是如何变化的也很重要。本文的贡献是基于情感文本分析的一种新方法，用于检测和预测Facebook评论的负模式和正模式，该方法结合了（i）实时情感文本分析用于模式发现和（ii）批量数据处理用于创建意见预测算法。为了进行预测，我们提出了两个步骤的算法：（i）使用无监督聚类技术对模式进行聚类；（ii）根据从某个聚类中找到最近的模式进行趋势预测。实例研究表明，该方法的有效性和准确性（平均mae=0.008）及其实际应用价值。此外，我们还发现了三种类型的用户态度模式并对它们进行了描述。
URL
https://arxiv.org/abs/1906.03392
