## 5 Opinion Aggregation and Summarisation
### Opinion Interestingness  
Typically, opinion aggregation involves simply combining all the positive and negative scores about a topic, document or corpus. However, there is some debate in the literature about the relative balance between positive and negative opinions in a document set. For example, [34, 35] found that extremely negative information can prompt spontaneous judgment formation in a way that extremely positive information does not: an excellent overview of this debate can be found in [36]. In some cases, people are more likely to post positive than negative reviews or opinions; in other cases, the opposite is true. Moreover, in product reviews, positive and negative comments outweigh neutral ones, but this is not necessarily true of news articles, for example, or even random tweet collections. Some variations have therefore been proposed, depending on the domain: such as additionally weighting neutral sentiment as slightly positive [37]. The theory behind this is that a neutral sentiment expressed actually has slightly more value than no sentiment expressed at all. So for example an equal combination of positive and negative sentiment expressed about a single topic swings very slightly towards the positive side and should be treated differently from something which contains no sentiment. In our system, the users in our test case considered that having a single opinion about a document (_i.e._, whether it is overall positive or negative) was not useful unless it was related to something specific (e.g., \"This document is positive about [PERSON]\"). However, a simple aggregation of document scores will not provide this information, because it is often impossible to associate a single entity as the target of the aggregated opinions. Furthermore, one cannot simply associate the overall opinion with the most frequent entity, or even an entity which is clearly the topic of the document, because not all opinions will be about this entity. On the other hand, knowing the range of diversity of opinions in the document is potentially useful, so that one can see whether the document is highly opinionated, and whether the opinions cover a wide range of topics. One possibility for finding interesting documents in terms of opinions is to find documents which contain outlying opinions about entities. This is in some sense the opposite of calculating reputation trust. There, a user is considered trustworthy if their opinions typically match those of the majority of users, since we typically trust the majority opinion about a topic (such as a book or film review). In ARCOMEM, however, we want to find things which are unusual, such as a lone voice of dissent. We have implemented an approach that involves finding entities with outlying opinion scores in a document, as follows:  
* For each entity which is the target of an opinion in the corpus, calculate the mean and standard deviation of the opinion scores for that entity (across all documents in the corpus). Opinions that do not have a specific target are included in the global (corpus-wide) statistics, but not in any target's statistics;
* Calculate the target-interestingness of each targeted opinion based on how far out of a range around that target's mean its opinion score is (opinion scores within the range have an interest score of 0). Calculate the global interestingness of each opinion the same way, but using the global statistics (mean and standard deviation);* Calculate the interest score of each document as the mean of the global interest scores of the opinions it contains. Outliers therefore make a document more interesting, and opinions close to the average make it less interesting. The approach is realised in the following way. After annotating and scoring all the opinions in a corpus as usual, we then:  
1. Scan through the whole corpus and build up a data structure that contains for each opinion annotation: * a document ID; * an annotation ID; * the target entity or event; * the score;
2.