Purpose

Social media is characterized by its volume, its speed of generation and its easy and open access; all this making it an important source of information that provides valuable insights. Content characteristics such as valence and emotions play an important role in the diffusion of information; in fact, emotions can shape virality of topics in social media. The purpose of this research is to fill the gap in event detection applied on online content by incorporating sentiment, more specifically strong sentiment, as main attribute in identifying relevant content.

Design/methodology/approach

The study proposes a methodology based on strong sentiment classification using machine learning and an advanced scoring technique.

Findings

The results show the following key findings: the proposed methodology is able to automatically capture trending topics and achieve better classification compared to state-of-the-art topic detection algorithms. In addition, the methodology is not context specific; it is able to successfully identify important events from various datasets within the context of politics, rallies, various news and real tragedies.

Originality/value

This study fills the gap of topic detection applied on online content by building on the assumption that important events trigger strong sentiment among the society. In addition, classic topic detection algorithms require tuning in terms of number of topics to search for. This methodology involves scoring the posts and, thus, does not require limiting the number topics; it also allows ordering the topics by relevance based on the value of the score.

Peer review

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-12-2019-0373

中文翻译：

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公众情绪：社交媒体在揭示重要事件中的作用

目的

社交媒体的特点是其数量，生成速度以及便捷的访问方式。所有这些使它成为提供有价值的见解的重要信息源。价和情感等内容特征在信息传播中起着重要作用；实际上，情感可以塑造社交媒体中话题的病毒式传播。这项研究的目的是通过将情感（尤其是强烈的情感）作为识别相关内容的主要属性，来填补在线内容上事件检测的空白。

设计/方法/方法

这项研究提出了一种基于强情感分类的方法，该方法使用机器学习和高级评分技术。

发现

结果表明以下主要发现：与最新的主题检测算法相比，所提出的方法能够自动捕获趋势主题并实现更好的分类。另外，该方法不是特定于上下文的。它能够在政治，集会，各种新闻和真实悲剧的背景下，从各种数据集中成功识别重要事件。

创意/价值

这项研究假设重要事件会触发社会强烈的情绪，因此填补了在线内容上主题检测的空白。此外，经典的主题检测算法需要根据要搜索的主题数进行调整。这种方法涉及对职位评分，因此不需要限制主题数量；它还允许根据分数的价值，按相关性对主题进行排序。

同行评审

本文的同行评审历史记录可在以下网址获得：https://publons.com/publon/10.1108/OIR-12-2019-0373