Deep learning, a subset of machine learning, continues to evolve, offering a wide range of applications, one of which is text classification. Text classification with deep learning has reshaped many industries by facilitating information management, sentiment analysis, spam detection, and more.

Text classification with deep learning harnesses Artificial Intelligence to process and classify large amounts of text data into predefined categories. Used in recommendation systems, customer service bots, and content classification systems, it is the backbone of many contemporary applications.

• High Accuracy: As it relies heavily on Artificial Neural Networks, classification accuracy is substantially higher compared to traditional machine learning methods.
• Automatic Feature Extraction: Feature extraction, which was traditionally done manually, can be fully automated with deep learning, thus saving time and improving accuracy.
• Resources and time-saving: As deep learning models are self-learning, they reduce the need for manual input, significantly saving time and resources for organizations.
• Scalability: Deep learning models scale very effectively, handling large amounts of data without compromising accuracy, which is a significant plus for big data applications.
• Self-learning: The ability of deep learning models to learn on their own makes them effective in dealing with complex language nuances, and therefore, efficient at text classification.

The successful implementation of text classification with deep learning begins with defining the problem statement and identifying suitable algorithms. You will need to gather and preprocess suitable text data, split it into training and testing datasets before you start training your model.

The next step involves optimizing your model by tweaking parameters or employing techniques such as dropout and batch normalization. Once the model is optimized and trained, you can test it and validate its accuracy on the testing dataset. If the model performs well on the testing data, it's ready for deployment.

• High Precision and Accuracy: Unlike traditional methods, deep learning techniques offer a much higher level of precision and accuracy by understanding complexities and nuances in the text.
• Multilingual: With deep learning, text classification can be applied effectively across languages - a game-changer in a globalized world where data is not restricted to one language.
• Scalability: Deep learning can handle massive datasets with ease, making it a scalable solution for text classification challenges in big data applications.
• Time-saving: The ability of deep learning to automate and streamline the text classification process results in substantial time economies, boosting productivity.
• Continuous Learning and Improvement: Deep learning models continue to learn from new data, further refining their performance and accuracy over time.

• Hardware Resource-intensive: Deep learning algorithms require robust hardware resources such as high-end GPUs which can increase implementation costs.
• Longer Training Time: While they offer higher accuracy, deep learning models often require more time to train compared to traditional machine learning models.
• Data Needs: Deep learning requires a large amount of labeled data to train effectively, making it a challenge for organizations with limited data available.
• Difficulty in Interpretation: The complexity inherent in deep learning models makes them difficult to interpret or explain – a factor sometimes referred to as the 'black box' problem.

In conclusion, while text classification with deep learning presents challenges like substantial hardware requirements and a longer training duration, its benefits such as high scalability, precision, and automation make it a promising tool to harness. Strategic planning and careful decision-making can mitigate some challenges and enable organizations to exploit the power of deep learning for text classification.