# LSRM: A New Method for Turkish Text Classification
## 4 Application of the Model
### Last Suffix Remove Method (LSRM)
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Derivational Affixes**} \\ \hline Derivational affixes that generate a noun & -luk, -lik, -li, -l-, -suz, -siz, -cil, -q, -cu, -ci, -q, -ce, -ca, -cl, -das, -incu, -inci, -msu, -msu, -cil, -cl, -sin, -ssl, -at, -cagiz, -ck, -cik, -t \\ Derivational affixes that generate verbs & -la, -le, -al, -l-, -a, -e, -ar, -da, -de, -et, -ik, -msa, -se, -kur, -len, -la, -las, -les, -sa, -se \\  & from nouns & -ce, -ceek, -cak, -ak, -ge, -kan, -gan, -gi, -gl, -ki, -gic, -gcn, -gun, -gin, -t, -u, -u, -tc, -tci, -tc, -tc, -ik, -ik, -tk, -mm, -im, -im, -in, -tin, -tin, -tin, -tin, -tin, -tin, -tin, -tin, -tr, ir, -er, -ur, -us, -gls, -it, -tt, -it, -im, -mek, -ti, -tmethod, the pseudocode of the proposed method is given in Code 1, and the details of the algorithm are explained as follows (Algorithm 1):  
```
Input: Set up lists for derivational affixes and inflectional affixes Initialize Affixes, A \(\leftarrow\) a sort the suffix lists by length in descending order for more efficient processing Initialize Files, F \(\leftarrow\) through a range from each file F(file)\({}_{1}\ldots\)F(file)\({}_{n}\) Initialize Words, W \(\leftarrow\) through a range from each word W(word)\({}_{1}\ldots\)W(word)\({}_{n}\) for all file a range from 1 to N to process each file  Define file path based on the loop index. TF \(\leftarrow\) Read text from the file and handle potential errors. TW \(\leftarrow\) Split the sentence into words. while end of file is a range from 1 to N to process each word if(Word Length(W) >= 3) && Has Suffix Exist(W)) \(\triangleright\) at least three letters or more  Split the filtered sentence into individual words for processing. Cut the last suffixes from word \(\triangleright\) affixes were removed from the words end if endwhile Continue this process until suffixes are removed. endfor Continue to the next iteration of the loop until all files are processed. ```  
**Algorithm 1**LSRM  
The first and second stages of the study focused on selecting datasets and identifying the algorithms, respectively. The third stage involved determining the performance metrics acquired by the experiments. The fourth stage included document preprocessing on the datasets. The study opened each file in each dataset and analyzed each word independently in the fifth stage. If the word was longer than three letters and had one of the derivational affixes or inflectional affixes listed in Table 4, the suffix was removed from that word. These generated and suffix-removed words were also saved in the newly created datasets, and in this way, it allowed for comparisons with the original datasets and the development of new datasets used for the LSRM method. The sixth stage involved algorithm-based vectorization operations for Word2 vec, Doc2 vec, Glove, and FastText algorithms using the newly created LSRM datasets. The study ran the algorithms using the newly created dataset versions in the seventh stage. Regardless of the method and algorithm, the study used 80% of the training data and 20% of the test data in all tests performed within the run. The final stage involved analyzing whether there were statistical increases in the classification success rates using the ANOVA statistical method on the previously acquired algorithm findings. The study utilized ML algorithms, DL algorithms, WEm algorithms, and transfer-learning algorithms to classify texts in the Turkish language using three separate datasets to measure the TC performance. It also developed a new method called LSRM to increase the TC performance for agglutinative languages like Turkish. The study also tested this approach on three separate datasets: TTC-3600, U-3000, and TbTT-2000. Figure 1 displays the workflow diagram.