## 6 Results and Discussion
### Analogy Test
#### 6.1.2 FastText Models  
For the FastText models, Figure 7 shows the overall results of the analogy test of 54 word2 vec models. Generally, the FastText models showed more stability in performance than the Word2 Vec models. Moreover, similar to the Word2 vec models, the Wiki corpus models outperformed all the models of the other corpora. Its hits score had an average score of 87. Generally, the skip-gram models performed better than the CBOW models despite the used corpus. This is related to the nature of FastText, which considers the words as chunks of characters, which means that they are formed by character n-grams. In general, similar to the Word2 Vec models, the Wiki corpus models remarkably outperformed all the models of the other corpora. It scored 86 at best, while the Watan and Book corpora scored 40 and 49, respectively. The Books corpus models showed better performance than the Watan corpus models. Moreover, we analyzed the results based on the used corpus in terms of the different values of the hyperparameters. For the Watan corpus, as illustrated in Figure 6b, both the CBOW and skip-gram models seem to show consistent performance across various vector sizes and window sizes. Consequently, the chosen approach, window size, and vector size have a limited impact on performance when using the Watan corpus. Figure 6: Model analogy test performance based on the window sizes of 5, 7, and 10 against the vector sizes 200, 300, and 400 for the CBOW and skip-gram approaches. (**a**) Word2 Vec models analogy test results for the Watan corpus. (**b**) FastText models analogy test results for the Watan corpus. (**c**) Word2 Vec models analogy test results for the Book corpus. (**d**) FastText models analogy test results for the Books corpus. (**e**) Word2 Vec models analogy test results for the Wiki corpus. (**f**) FastText models analogy test results for the Wiki corpus. On the other hand, for the Books corpus, the analogy test results for both models are relatively high and consistent across different vector sizes for most window sizes. Both the CBOW and skip-gram models tend to perform well in these evaluation settings, with the analogy test results ranging from 42% to 49%. Moreover, for a window size of 7 and 10, skip-gram consistently outperforms CBOW, achieving slightly higher analogy test results, as illustrated in Figure 6d. Generally, a window size of 10 had a noticeable impact on the Book models. For the Wiki corpus, both the CBOW and skip-gram models exhibit strong analogy test results, as illustrated in Figure 6f. Indeed, skip-gram consistently outperforms CBOW, indicating that it captures more semantic relationships and performs better on the analogy test. More specifically, both models show strong performance, with CBOW performing slightly better at a window size of 5, where it reached 79% to 80%, while the skip-gram reached 74% to 75% for different vector sizes. Moreover, skip-gram performs consistently well across vector sizes, with a notable performance improvement compared to CBOW for a window size of 7, with analogy test results ranging from 88% to 90%. In fact, it has the best analogy results across all 108 models. For a window size of 10, both the CBOW and skip-gram models' results are consistently high and range from 87% to 88% for different vector sizes. Unlike vector size, the chosen approach and window size tend to affect the results. To summarize, for all models, training with a large vocabulary size, employing skip-gram, and utilizing a large window size can lead to good performance. Conversely, training with a small-vocabulary-size corpus can result in low analogy results and unstable performance. While Word2 Vec demonstrated similar performance for CBOW and skip-gram, the FastText models achieved better results with the skip-gram approach. Broadly speaking, there is not a widely recognized standard for evaluating the quality of analogy test outcomes. Consequently, we relied on [PERSON]'s research results [3] as a benchmark to gauge the outcomes of our own work.