Video advertisement mining for predicting revenue using random forest

Shaken by the threat of financial crisis in 2008, industries began to work on the topic of predictive analytics to efficiently control inventory levels and minimize revenue risks. In this third-generation age of web-connected data, organizations emphasized the importance of data science and leveraged the data mining techniques for gaining a competitive edge. Consider the features of Web 3.0, where semantic-oriented interaction between humans and computers can offer a tailored service or product to meet consumers\u27 needs by means of learning their preferences. In this study, we concentrate on the area of marketing science to demonstrate the correlation between TV commercial advertisements and sales achievement. Through different data mining and machine-learning methods, this research will come up with one concrete and complete predictive framework to clarify the effects of word of mouth by using open data sources from YouTube. The uniqueness of this predictive model is that we adopt the sentiment analysis as one of our predictors. This research offers a preliminary study on unstructured marketing data for further business use

A new strategy for case-based reasoning retrieval using classification based on association

Cased Based Reasoning (CBR) is an important area of research in the field of Artificial Intelli-gence. It aims to solve new problems by adapting solutions, that were used to solve previous similar ones. Among the four typical phases - retrieval, reuse, revise and retain, retrieval is a key phase in CBR approach, as the retrieval of wrong cases can lead to wrong decisions. To ac-complish the retrieval process, a CBR system exploits Similarity-Based Retrieval (SBR). How-ever, SBR tends to depend strongly on similarity knowledge, ignoring other forms of knowledge, that can further improve retrieval performance.The aim of this study is to integrate class association rules (CARs) as a special case of associa-tion rules (ARs), to discover a set (of rules) that can form an accurate classifier in a database. It is an efficient method when used to build a classifier, where the target is pre-determined. The proposition for this research is to answer the question of whether CARs can be integrated into a CBR system. A new strategy is proposed that suggests and uses mining class association rules from previous cases, which could strengthen similarity based retrieval (SBR). The propo-sition question can be answered by adapting the pattern of CARs, to be compared with the end of the Retrieval phase. Previous experiments and their results to date, show a link between CARs and CBR cases. This link has been developed to achieve the aim and objectives.A novel strategy, Case-Based Reasoning using Association Rules (CBRAR) is proposed to improve the performance of the SBR and to disambiguate wrongly retrieved cases in CBR. CBRAR uses CARs to generate an optimum frequent pattern tree (FP-tree) which holds a val-ue of each node. The possible advantage offered is that more efficient results can be gained, when SBR returns uncertain answers. In addition, CBRAR has been evaluated using two sources of CBR frameworks - Jcolibri and Free CBR. With the experimental evaluation on real datasets indicating that the proposed CBRAR is a better approach when compared to CBR systems, offering higher accuracy and lower error rate