## 4 Results and Discussion
### Discussion on Performance of Keyword-Based Vulgarity Extraction and Classification Baselines
#### 4.1.1 Discussion on Ratio of Vulgar Words Extracted with TF-IDF Weighting  
To estimate the potential of the method for automatic extraction of vulgar words, we first calculated the _TF-IDF_ scores for each word in both the vulgar and non-vulgar parts of the dataset. Then, we manually determined the ratio of words that were actually vulgar within the top 10, 20, 30, etc., word spans from that list. As TF-IDF has the well-recognized potential to place words that are the most relevant for each compared group (here vulgar vs. non-vulgar) at the top of the list, this would show the accuracy of using only TF-IDF to extract vulgar words. The results show that the method achieved the extraction ratios of 0.8, 0.75, 0.667, 0.6, 0.58, 0.583, 0.557, 0.525, 0.489, and 0.45 within the top 10, 20, 30, 40, 50, 60, 70, 80, 90, and 100 extracted words, respectively. The high accuracy at the beginning of the list indicates that the majority of extracted words with high _TF-IDF_ scores were indeed vulgar. However, the lower accuracy in the later word spans suggests roughly half of the extracted words, although being statistically relevant to the vulgar group, were indeed not vulgar. Table 4 and Figure 9 represent the extraction accuracy for a wider span up to the first thousand words extracted with TF-IDF. As one can see, at the end of the list, only about sixteen percent of the extracted words (i.e., 161 in 1000 words precisely) were indeed vulgar. This suggests that using only TF-IDF without any additional filtering of non-vulgar words will not yield high scores and will not be practical in the long run. Therefore, we needed to improve the extraction method with an additional algorithm for filtering out the words that were most certainly non-vulgar from the group extracted with TF-IDF. 1.2 Baseline 1: Keyword-Matching Method Based on TF-IDF Term Extraction with No Additional Filtering  
To verify the practical usability of the automatic vulgar term extraction method, we applied the automatically extracted terms as a lexicon (word list) in a simple keyword matching-based method for vulgar sentence detection. In this method, we performed keyword matching without any additional filtering of the terms extracted with the TF-IDF scores. This means that in the list of the extracted terms there might be some non-vulgar words. As vulgar remarks can be expressed without specifically vulgar terms, this could either improve or impair vulgar sentence detection. The results indicated an accuracy of the detection of vulgar sentences at levels of 0.197, 0.236, 0.255, 0.269, 0.280, 0.293, 0.32, 0.343, 0.352, and 0.36 for detection when only the words from the top 10, 20, 30, 40, 50, 60, 70, 80, 90, and 100 of the list of words extracted with TF-IDF (See Table 4 and Figure 9 for reference) were used, respectively. The accuracy suggests that the method successfully identified a significant number of vulgar sentences from the dataset. Interestingly, even using only the top 10 words allowed for a close to 20% accuracy. This is even more impressive if we acknowledge that some of those words were not specifically vulgar. However, the overall low accuracy, reaching only 62%, indicates that (1) some non-vulgar sentences were incorrectly matched to the list of automatically extracted vulgar term keyword candidates (false positives), and (2) many vulgar sentences were not matched due to the limitations of purely keyword-matching-based method. This shows that there is a wide range of vulgar sentences where the vulgar meaning is not expressed with any specifically vulgar terms. Moreover, although the keyword-matching-based methods are advantageous in terms of processing speed, they have limited applicability, which confirms similar findings from previous research [86; 87; 88]. 4.1.3 Baseline 2: Keyword-Matching Method Based on TF-IDF Term Extraction with Only Manual Filtering  
In this method, after extracting the vulgar keyword candidates automatically using the TF-IDF, we filtered out the non-vulgar words manually.