Tokenization for Text Analysis
Authors:Sowmik Sekhar
Abstract-The Seminar “TOKENIZATION FOR TEXT ANALYSIS” is an advanced tokenization technique that is a revolutionizing text analysis, enabling researchers to glean profound insights from vast textual data. This study explores diverse tokenization approaches, encompassing word-based, subword-based, character-level, and language-agnostic methods, with a particular emphasis on BERT integration for capturing language nuances. Striking a balance between granularity and computational efficiency is paramount for practical applications in sentiment analysis, information retrieval, and natural language processing, where processing massive datasets while preserving language intricacies is essential. The study addresses challenges posed by social media content with informal language and unconventional writing styles, unsegmented languages lacking defined word boundaries, and multilingual datasets demanding language-independent tokenization strategies. For large-scale text analysis, optimizing tokenization to minimize processing time while maintaining analysis performance is critical, making tokenization a viable approach for real-world applications. This research provides valuable insights into aligning tokenization methods with text data characteristics and analysis goals, ensuring granularity matches task requirements. Furthermore, the study envisions seamless integration of advanced tokenization techniques with emerging NLP technologies, enhancing text analysis efficacy across domains for knowledge discovery and informed decision-making. Subword-based tokenization approaches, such as Byte Pair Encoding (BPE) and Sentence Piece, effectively capture language nuances and improve the performance of NLP tasks on social media data and other text datasets with informal language and unconventional writing styles. These methods break down words into smaller units, enabling a more granular representation of language. For multilingual datasets and unsegmented languages with undefined word boundaries, language-agnostic tokenization methods, such as those based on characters or word embeddings, prove to be valuable tools. These methods overcome the limitations of language-specific tokenization approaches and effectively handle diverse linguistic structures, making them well-suited for cross-lingual applications.
