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].
3.4.1.1 Discussion and Result
In this demonstration, they analyzed discrete arrangements. Those are mainly based on aspect-based sentiment analysis. The results we can see in Tables 3.2 and 3.3. They stated the results picked up with AFINN sentiment analysis algorithm, due to space reasons. It did not come out with any major dissimilarity with the CoreNLP algorithm. As it is based on CF user-based approach, on Yelp dataset and Tripadvisor dataset, they took top 10 aspects from the datasets. Besides, the above results are better than the previous 50 aspects. Accordingly, they did not take a bigger space.
One more attractive result comes from the Yelp and TripAdvisor by use of sub-aspect which gave a significant improvement in performance. Here, the maximum efficiency came by using the top 50 aspects. For a better understanding of this, we need to do further investigations [5, 20].
Table 3.2 Result comparison.
Result of MCRS-Based CF Experiment 1
Result of Experiment 2
| Configuration | Dataset | Dataset | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| #neigh. | #asp. | Sub-asp | Yelp | TripAdvisor | Amazon | Configuration | Yelp | TripAdvisor | Amazon | |
| 10 | 10 | Y | 0.8362 | 0.7111 | 0.6464 | Multi-U2U | 0.8362 | 0.7111 | 0.6276 | |
| 10 | 10 | N | 0.841 | 0.7564 | 0.6335 | U2U-Euclidean | 0.886 | 0.8337 | 0.7254 | |
| 10 | 50 | Y | 0.841 | 0.7269 | 0.6346 | U2U-Pearson | 0.964 | 1.1222 | 0.9789 | |
| 10 | 50 | N | 0.8364 | 0.8007 | 0.6276 | Static-Multi-U2U | N.A. | 0.798 | N.A. | |
| 30 | 10 | Y | 0.8461 | 0.7677 | 0.712 | Multi-I2I | 0.864 | 0.8245 | 0.811 | |
| 30 | 10 | N | 0.8473 | 0.7722 | 0.7122 | I2I-Euclidean | 0.8745 | 0.8429 | 0.8117 | |
| 30 | 50 | Y | 0.8474 | 0.7743 | 0.7101 | I2I-Pearson | 1.1794 | 0.8644 | 0.9679 | |
| 30 | 50 | N | 0.8494 | 0.8003 | 0.714 | Static-Multi-I2I | N.A. | 0.8474 | N.A. | |
| 80 | 10 | Y | 0.8579 | 0.7971 | 0.7584 | RatingSGD | 0.8409 | 0.745 | 0.8859 | |
| 80 | 10 |
N
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