Survey of Text Mining Techniques, Challenges and their Applications
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Title: Survey of Text Mining Techniques, Challenges and their Applications

Section 1: Introduction
Description 1: Introduce the topic of text mining and provide an overview of its relevance and significance in various fields.

Section 2: Text Mining Techniques
Description 2: Discuss different techniques used in text mining, including methods from information retrieval, machine learning, statistics, computational data sciences, and advanced data mining.

Section 3: Applications of Text Mining
Description 3: Describe various applications of text mining in different domains and how these techniques are utilized to extract meaningful patterns from textual data.

Section 4: Rough Set Based Text Mining Techniques
Description 4: Explore the specific rough set based text mining techniques, explaining their mechanisms and benefits in the context of text mining.

Section 5: Challenges in Text Mining
Description 5: Discuss the current challenges faced in the field of text mining, such as data quality, computational complexity, and the need for more advanced algorithms.

Section 6: Future Directions and Research
Description 6: Outline potential improvements and future research directions in text mining, highlighting areas that require further exploration and development.

Section 7: Conclusion
Description 7: Summarize the key points discussed in the paper and provide final thoughts on the state and future of text mining.