## 6 Opinion Event Detection in the Social Web  
Social media data has the form of a continuous stream of newly created documents. In contrast to previous methods such as polls which only allowed sampling at discrete time points, this makes continuous and real-time monitoring possible. It allows us therefore to get faster and deeper insights, but makes it a necessity to develop automatic methods to analyze and summarize the data to keep up with the huge number of documents produced. The use of opinion mining on social media data can be seen during the 2012 US presidential election. In the run-up to the election, many news organizations used opinion timelines, especially from Twitter, in their reporting, and Twitter itself provided a real-time chart of the opinions towards the two presidential candidates, called Twindex[38]. Typically such opinion timelines show the average opinion or the sum of opinions towards one or more entities over time (see Figure 3). They are useful to illustrate the public's reaction towards external events. However, on their own the timelines are hard to understand, as the cause for opinion changes is not apparent from the chart. Careful interpretation, typically by experts with the necessary background knowledge, is necessary to gain a better understanding. This limits the usefulness of opinion timelines, because the potential users have neither the necessary background knowledge nor access to domain experts. By developing methods that increase the understandability of opinion timelines, we can make social media document collections more useful to users such as researchers, journalists and analysts. For example, journalists can use these methods to understand the opinions during a major political event in a foreign country (e.g., the Arab Spring) and historians or political scientists can use them as a starting point when exploring a web archive several years or decades later. One established way to make sense of large amounts of social media data is \"event detection\", where the frequency of mentions of given entities is tracked over time and sudden increases in that frequency are taken as indicators of an ongoing event involving that entity. Such sudden increases are typically called \"bursts\", while the class of algorithms that detect such bursts are called \"burst detection algorithms\" accordingly. In this work, we transfer this approach to the mining of opinion timelines: instead of looking at the frequency of entity mentions, we look at several timelines describing opinions, and try to detect bursts in them. We hypothesize that the resulting events are a subset of the events found using equivalent event detection methods, their defining characteristic being that they reflect an event where social media users are emotionally more involved. As a result, these so-called _opinion events_ are very useful to understand the ongoing developments. Figure 3: Typical representation of opinions over time (from [22]). This figure compares an estimation of the opinions in the blogosphere towards the two candidates of the 2008 US presidential election to traditional opinion polls. To allow a quick understanding of a timeline, a good labeling of detected events is essential. We provide labeling of opinion events using tag clouds of descriptive keywords. This method is widely used and provides an easily understood way to summarize the underlying documents. In this work, we concentrate on opinion event detection from blog documents, as the text in blog documents is typically longer, and the language is of higher quality and therefore easier to process with existing tools. However, the algorithm is agnostic to the type of data processed and can be easily adapted to other data sources. Our main contributions to this topic are:  
* We propose an unsupervised approach to detect opinion events from social media documents.