Text Classification for Azerbaijani Language Using Machine Learning and Embedding
This work addresses text clustering and classification for Azerbaijani language applications such as news categorization and sentiment analysis, but it is incremental as it applies standard methods to a new language.
The paper tackles text classification for the Azerbaijani language by applying existing machine learning techniques like Naive Bayes, SVM, and Decision Trees, but does not report specific performance numbers or results.
Text classification systems will help to solve the text clustering problem in the Azerbaijani language. There are some text-classification applications for foreign languages, but we tried to build a newly developed system to solve this problem for the Azerbaijani language. Firstly, we tried to find out potential practice areas. The system will be useful in a lot of areas. It will be mostly used in news feed categorization. News websites can automatically categorize news into classes such as sports, business, education, science, etc. The system is also used in sentiment analysis for product reviews. For example, the company shares a photo of a new product on Facebook and the company receives a thousand comments for new products. The systems classify the comments into categories like positive or negative. The system can also be applied in recommended systems, spam filtering, etc. Various machine learning techniques such as Naive Bayes, SVM, Decision Trees have been devised to solve the text classification problem in Azerbaijani language.