Textual data presents additional problems, such as erroneous spelling and sentence structure, which makes it difficult to extract the relevant information and analyze it. During the text mining process, important difficulties and obstacles such as domain knowledge integration, variable concept granularity, multilingual text refining, and natural language processing ambiguity occur. Synonyms and antonyms are all used in texts, which causes issues for text mining techniques that take both into account. When a collection of documents is vast and comes from several disciplines in the same domain, categorizing them might be challenging.