In this paper, studies determining abbreviations and their meanings in job texts are explained. The data used in this study consist of job texts stored in the Kariyer.net database. The applied method consists of two separate steps: first, the words and phrases in all job text documents are vectorised with the Word2Vec model. The phrases and abbreviations that are compatible with each other in the proximity of these word vectors are then checked and matched. In the second step, sentences with abbreviations and their meanings in the dataset are defined by the rules determined by Regex. Then, the appropriate abbreviations are collected and added to the dictionary.   Keywords: Word embeddings, text mining, abbreviation detection.  

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Text mining studies on job ads have become widespread in recent years to determine the qualifications required for each position. It can be said that the researches made for Turkish are limited while a large resource pool is encountered for the English language. Kariyer.Net is the biggest company for the job ads in Turkey and 99% of the ads are Turkish. Therefore, there is a necessity to develop novel Natural Language Processing (NLP) models in Turkish for analysis of this big database. In this study, the job ads of Kariyer.Net have been analyzed, and by using a hybrid clustering algorithm, the hidden associations in this dataset as the big data have been discovered. Firstly, all ads in the form of HTML codes have been transformed into regular sentences by the means of extracting HTML codes to inner texts. Then, these inner texts containing the core ads have been converted into the sub ads by traditional methods. After these NLP steps, hybrid clustering algorithms have been used and the same ads expressed with the different sentences could be managed to be detected. For the analysis, 57 positions about Information Technology sectors with 6,897 ad texts have been focused on. As a result, it can be claimed that the clusters obtained contain useful outcomes and the model proposed can be used to discover common and unique ads for each position.