# Digital Qualitative and Quantitative Analysis of Arabic Textbooks
## 5 Case Study: Analysis of a Collection of Arabic Textbooks
The shape of an Arabic character may significantly vary, and this variation depends on the position of the character within a word and its adjacent characters [31]. The fact that Arabic text is written from right to left, is cursive in nature, and can be written in a variety of fonts also means that after the OCR processing, words may be hyphenated or overlap, rendering the document illegible. Arabic words also have diacritics that affect their pronunciation and/or their meaning. The linguistic and grammatical aspects are also a challenge; in the Arabic language, a prefix or suffix could change the whole meaning of a word or make it impossible for a digital tool to recognize the word. Therefore, in order to improve our frequency analysis results, we propose the creation of black and white lists. This will be addressed in detail under the section on frequency analysis. Returning to the steps carried out by researchers within the Toolbox, we will discuss the natural language processing (NLP) activities such as topic modelling, which serves to detect word and phrase patterns within a set of documents, and the automatic  
Figure 4: Project summary dashboard. clustering of word groups and similar expressions that best characterize these activities. These processes help to reveal the abstract \"topics\" that occur in a collection of documents by determining the probability distribution of topics throughout the text (see Figure 5). Researchers can change the parameters based on their analysis needs and apply filters to group the metadata to improve their research results. The specific aims of the individual analysis will dictate the choices made by the researcher throughout the different steps. In the frequency analysis, the Edumeres Toolbox shows the overall word frequencies for the entire corpus as well as the information about how word frequencies are spread out across the documents within the corpus. The researcher can control the process and refine the results based on his/her needs by applying filters to omit words that may affect the results and by adding similar terms to exclusion lists, for example. Excluded words are those that are defined as irrelevant to the analysis or those listed in the abovementioned black list. This functionality can be used, for example, to remove words that have been incorrectly extracted by OCR to make sure the data in the frequency reports are clean. In addition, users can use \"stop-word\" lists specific to each language. These include so-called \"function words\" that do not carry much meaning, such as determiners, prepositions, definite and indefinite articles, conjunctions, or numerals (the, a, in, is, at, which, on, to, from, etc.). Stop-word lists can be manually curated for each language, and these words are filtered out before the data (text) undergoes natural language processing, or after. In our research study, for example, we wanted to examine the number of times words such as \"peace\", \"culture\", \"religion\", etc., appeared in each textbook and to compare the results according to metadata categories such as subject, grade, and year (see Figure 6). To do this, we selected the stop-word list for the Arabic language, removed/added prefixes and suffixes as needed, and performed the frequency analysis. By interpreting the results, we were able to draw conclusions, answer our research questions, and prepare a report detailing the findings, interpretations, and conclusions. Figure 5: Topic modelling. For instance, we wanted to know how many times the word \"peace\" or \"_\"_\" (_salm_) appears in each textbook, how the use of the term changes from year to year or from grade to grade, and in which context it appears. When we selected the word on its own, without excluding any similar words, the tool also counted the word '_\"_\" (_Islam_), which means \"Islam\" (the religion), making our research inaccurate. The fact that the two words are very similar made it hard for the machine to recognize the difference.