## 10 Evaluation Results  
Entity-based and topic-based search for the U.S. Elections 2012 campaign archive revealed the impact that well designed data visualization has over simple long lists of metadata. In fact, the combination of topics, entities and opinions in an interactive visual search resulted in completion of search tasks multiple times faster than the entity-based ones. The average completion times of the topic-based searches were, on average, more than eight times lower than the entity-based ones, as calculated by the system. This denotes the advantage of advanced visual analytics to information understanding, especially if complex semantic links are present in the data. Entities and opinions were found to be the most useful metadata that the end users required. Entities were primarily used for querying the archives and refining the search within the SARA interface. Opinions were naturally bound to the entities. The requirements of that time stressed the need for exposing the opinions and their target entities, optimally extracting the opinionated sentences from the web documents and making them available for search. Topics were individually evaluated during exploratory pluralistic walkthroughs for the U.S. Elections 2012 session. The participant list included all the evaluation users, as well as the members of the design team. Earlier evaluation session had taken place around the middle of the design life-cycle when the topic detection module became available. The goal was to measure the importance of topics within the scope of web archive analysis and subsequently explore novel ways for representing topics to an optimal value within the context of SARA. As a result, topics, just like entities, were present on the main result page, on the web resource page, as well as exclusive visual overview, for the purpose of topic-based searching. The results of this evaluation verified that the topic-based search was a significant asset of the SARA interface. The accuracy and descriptive nature of the results of the visual representation for the relation between topics, main entities per topic and opinions for those entities were very successful. Topics were rated quite high in terms of importance in both datasets (Figure 5). The advanced topic visualisation was an experimental approach available for evaluation only for the U.S. Elections 2012 dataset. In the investment for growth evaluation, the topics were suggested by the SARA application only after the queries and subsequent user actions retrieved enough entities to partially describe a topic. Effectively, the users could not reveal the topics until they searched for a sufficient number of entities to identify at least one topic. On this dataset, the quality of the topics was perceived as worse (Figure 6), mostly due to the generic nature of the investment for growth dataset. Importance of topics was reported identical for both data set, while acceptance in the investment for growth was deemed much worse than on the U.S. Election 2012, due to both quality and the lack of the advanced topic visualisation feature connecting the topics with their context (_i.e._, entities and opinions). The ETOEs, enrichments and clusters were also fully evaluated in the investment for growth sessions. That dataset used the metadata generated by the final version of the ARCOMEM modules. Entities,  
Figure 5: Perceived metadata importance. Figure 6: Perceived metadata quality. opinions and enrichments were found to be more accurate, while events much less so. A critical claim was that event titles were incomprehensible sometimes. Enrichment and clustering provided additional value for the entities that were closely examined for specific web resources (Figure 6). A major comment about enrichment was a suggestion for improvement. The users suggested that the enrichment could suggest the best resource at the top of the list, instead of providing the full list of the, sometimes, identical or supplementary resource links.