Читать книгу Advanced Analytics and Deep Learning Models - Группа авторов - Страница 63

In this research activity, Musto et al. proposed a CF technique based on MCRS, which utilizes the information to analyze users’ interests conveyed by users’ reviews.

In their experimental data analysis, they use many traditional models for evaluation. The outcomes showed the perception in back of this research [5].

Now, if we look in their experimental data analysis, then we can see that they have used three datasets. Those are Yelp, TripAdvisor, and Amazon.

Table 3.1 Dataset statistics.

	Yelp	TripAdvisor	Amazon
Users	45,981	536,952	826,773
Items	11,573	3,945	50,210
Rating/Reviews	229,906	796,958	1,324,759
Sparsity	99.95%	99.96%	99.99%

This framework is mainly for aspect extraction and sentiment analysis. For implementing this, we need different types of parameters. In the first step, we need to remove the words like “a”, “and”, “but”, “how”, “or”, and “what”. In the next step, we need to set the framework in between 10 and 50 for extracting the aspects and sub-aspects. To calculate the efficiency of sub-aspects, the main aspects were extricated, in some experimental session. As per the sentiment analysis algorithmic program, both “CoreNLP” and “AFINN-based” algorithms were used. They set KL-divergence score value as 0.1. They used both user-based and item-based CF system. Previously, they have used an advance version of Euclidean distance, which they introduced as multi-dimensional Euclidean distance for calculating the neighborhood. By their formula for all the dataset, neighborhood size was set to 10, 30, and 80, and they did it because the bigger neighborhoods will reduce in the efficiency of the proposed algorithm [5].

The effectiveness of their algorithmic program was planned by calculating the average of the Mean Average Error (MAE). Rival framework is used to calculate the matrices, to make sure the dependability in results [5].