# LSRM: A New Method for Turkish Text Classification
## 4 Application of the Model
### Experimental Study, Analysis, and Results  
This study generated a software system using Python software language v3.8.5. It also used Natural Language Toolkit (NLTK) v3.5 for NLP. Numpy v1.19.2, which enables multidimensional array and matrix processing and analysis in Python language, was used in the study. For the plotted graphics, the study preferred Matplotlib library and Seaborn v.0.11.0 version, a data visualization library built upon the Matplotlib library. Additionally, the study utilized Gensim version 4.0.1 for vectoring structures and Pandas v1.1.3, a Python program, for data processing and analysis. Researchers performed their computer-based experimental studies on a computer with 16 GB of local memory, an Intel i7 7700 hq 64-bit processor, and a Nvidia GTX 1050 graphics card, preferring Linux Ubuntu as the operating system. The TTC-3600 [2] and Ulakbim-2000 [38] datasets used in the study are public datasets that are currently open and already available for the use of any researcher and have open access to these datasets from the studies cited [2,38]. The last dataset used in the study, the TBTT-2000, was generated by the National Library using bibliographic images that were transferred to the computer and by utilizing the fact that the state had made it publicly accessible to researchers [53]. In the ML algorithms-related section, the study analyzed the average success rate of TC on three different datasets and calculated it as 76.0% for ML algorithms. However, using the LSRM method, when combined with ML algorithms, slightly improved the classification success rate; as a result, it increased to 76.6%. While the LSRM method achieved higher success rates in 11 of the 15 tests, it only failed in three algorithms and remained the same in one algorithm. Table 6 displays the experimental results for the ML algorithms. In Tables, bold parts show high ACC rates. Figure 1: The pipeline of the workflow. Regarding the DL algorithms, analysis of the classification success rates on three different datasets revealed that the success rate of DL algorithms was significantly higher than the ML algorithms, with a rate of 91.2%. Additionally, the use of DL algorithms together with the LSRM method slightly increased the success rate, reaching 91.3%. The FM value also supported the ACC rates obtained consistently, similar to the results of the ML algorithms, since the datasets are in a balanced structure. Table 7 presents the experimental results for the DL algorithms. Intending to improve the success rate of DL algorithms, WEm-based Doc2 Vec, Word2 Vec, Glove, and FastText algorithms were used together since DL algorithms achieved a higher classification success rate than ML algorithms. Accordingly, using these vector-based algorithms combined with DL algorithms resulted in an average ACC rate of 86.39%. However, this rate lagged behind the average success rate of the DL algorithm. When the proposed LSRM method was combined with WEm based and DL algorithms, the average success rate improved by more than 3% compared to the WEm + DL results, increasing up to 89.62%. When analyzing the success rate of the LSRM method by itself, 31 of the 36 tests achieved a greater success rate through the sole application of the LSRM method. Table 8 displays the success rates of the experimental results for vector-based + DL algorithms.