{'fraction_non_alphanumeric': 0.027405602923264313, 'fraction_numerical': 0.004263093788063338, 'mean_word_length': 5.0851851851851855, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Multiple Bangla Sentence Classification using Machine Learning and Deep Learning Algorithms

Text classification is an essential and the most well-known topic of Artificial Intelligence as a discipline of Natural Language Processing (NLP). Because of the abundance of textual documents in Bangla, text classification has become a crucial subject. Natural Language Processing (NLP) in Bangla, at the same time, is not as developed as it is in English, and little study has been done in the context of the Bengali dialect, which is among the most widely used languages in the world. As a consequence, it's past time to address this issue in order to effective information management and data structure. The following is an example of a Bangla phrase from a narrative: assertive, interrogative, imperative, optative, or exclamatory text document. Numerous machine learning (ML) and deep learning (DL) algorithms are applied to categorize the sentence in the text document using the dataset. Our dataset is unique in that it was created by hand while keeping Bangla's sentence structure and origin in perspective. Within all the machine learning (ML) techniques, there are two that stands out: RN and DT provides the supreme exactness at 89.42%. As a deep learning strategy, between LSTM and RNN, LSTM exhibit superlative accuracy, with having an accuracy of 88.2 percent. Our experiment also offers a benefit in NLP for detecting the expression of textual data in the future execution, and hybrid approaches will be performed by increasing our dataset for improving the interaction between Bangla and the Natural language processing (NLP) field.