24-2 콘텐츠프로그래밍기초 기말 프로젝트 개인 과제

Corpus-based temporal word frequencies and trend analysis.

Learn Javanese (basa Jawa) and Javanese script (aksara Jawa) by memorizing them with Anki. Word frequency table also provided

Collection of Natural Language Processing laboratory exercises exploring text processing, linguistic analysis, and statistical methods.

A web application that allows users to upload and analyze PDF documents. The tool provides instant text analysis including word count, character count, sentence statistics, and word frequency visualization. Users can easily upload files through drag-and-drop or file selection, and toggle between including or excluding common words in analysis.

A full-stack text analysis application with Keycloak authentication, implementing word tokenization, sentence boundary detection, and longest word extraction algorithms with Redis caching for performance optimization.

See how often a Japanese word appears in everyday conversations, Netflix, YouTube, Wikipedia, and more.

A growing collection of Python problems covering algorithms, data structures, string manipulation, optimization, and data science simulations — solved with clean code, complexity analysis, and real-world context.

A tool to search for n-grams within ancient Latin texts and display their frequency

A comprehensive collection of Japanese word frequency datasets, consolidating datasets into unified formats for language learning, linguistics, and NLP research.

FALL 2025 LINGUIS R1B Research Essay, NLP Python Scripts By Shiyi (Yvette) Chen, UC Berkeley

Data science projects focused on exploring and analyzing Game of Thrones dialogues and characters using natural language processing (NLP) techniques, including sentiment analysis, word frequency, and named entity recognition.

Automate merging of DOC, DOCX, and PDF files with word frequency analysis. Streamlines document consolidation for large-scale projects.

♦️ Twitter US Airline Sentiment Analysis ♦️ Applied text preprocessing, NLP, and sentiment classification to analyze positive, neutral, and negative tweets. Created visualizations like sentiment distribution, airline comparisons, and word clouds for key insights. Delivered a cleaned dataset, insightful analysis, and automated PowerPoint report.

A Python tool to extract, clean, and analyze text from Word (.docx) files, showing the most frequent words.