## 3 Results
### Dataset Used and the Results of the Operation Type Recognition Using Comments  
The dataset used in these experiments consisted of records found in ledgers of 12 Dutch companies. The dataset was obtained legally with all permissions to use data for research purposes. The initial dataset contained nearly one million records. It was found that about 38 percent of all records did not have any comments. When records without comments were removed, about 500 thousand records were left for further analysis (more precisely, 498 thousand records). Some examples of the comments found in the ledgers are presented in Appendix A (2). First, bag-of-words have been applied to evaluate the content of the vocabularies used in different types of operations. It has been expected that rapid evaluation using BoW will allow finding if further and more detailed analysis can provide useful results. The BoW analysis led to the following observations. In many cases, it was found that with some types of operations, relatively few words are used, while some words are used very often in this type of operations. e.g., in comments with shareholders' properties, only seven different words made up 57 percent of all words used. At least one of those seven words has been used in 98 percent of records with shareholders' properties. In comments next to operations with salaries, two words were used almost always. This was the name of the month (used in 97 percent of comments from this group), and the abbreviation ljp is used exclusively in the comments next to operations with salaries. In comments next to depreciation operations, the words afschriyingen, afschr, and afs are used in 95 percent of records. In 92 percent of comments from this group, the name of the year is also used. On the other hand, in comments next to goods return operations, there are no dominant words. The three most often used words make only up to 8 percent of vocabulary in this case. The most frequently found word (Dutch word retour in this case) was found in only 9 percent of all comments. In a group called other operations, the digits 17, 2017, 18, 2018, 19, 2019, 20, and 2020 (these digits mean the year in this case) are found more often than in other types of operations. However, the year meaning digits make only 0.6 percent of all words found in this group of comments. In the group called other financial operations, the words stg, molli, and payment are found in about 40 percent of comments. In these operations there are many difficult to decode (from a computational point of view) abbreviations such as t. or m. The very characteristic property of these comments is the use of words belastingdienst salari. They are found in up to 15 percent of all comments. The sales operations make up the most significant part of all operations. The vocabulary of these comments is very diverse, but some words or abbreviations are found only next to these operations, e.g., abbreviations bv, b.v. or b.v are found exclusively in comments next to sales operations (13 percent of all comments). The abbreviation nl is found in about 20 percent of comments next to sales operations. The quite common situation is to find the address (street, city, etc.) in comments with this group of operations. This is very non-typical for other types of operations. These observations allow us to conclude that comment analysis has the potential to help identify the financial operations stored in ledger books. Cosine similarity was used to evaluate the similarity between vocabularies used in different types of financial operations. Table 1 shows cosine similarities between vocabularies used in different financial operations:  
The results clearly indicate that vocabularies used in comments next to different types of financial operations are essentially different. The biggest vocabulary similarities were found between residual operations and other operations, between residual and depreciation operations, and between shareholder and return operations. Even then, similarity is not very strong.