b06702041

b06702041's Stars

• northwesternfintech/2025QuantInternships

  Public quant internship repository, maintained by NUFT but available for everyone.

  1.3k93

• SuperCV/Book

  :green_book:我的个人书籍学习和收藏

  Language:Python2.8k1.1k

• monpa-team/monpa

  MONPA 罔拍是一個提供正體中文斷詞、詞性標註以及命名實體辨識的多任務模型

  Language:Python24526

• donghyeonk/han

  TensorFlow implementation of Z. Hu et al. "Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction", WSDM 2018

  Language:Python2918

• BastianF/Hourly-historical-Google-Trends-Data

  This Jupyter Notebook shows how to obtain hourly long term Google Trends data with proper scaling.

  Language:Jupyter Notebook1611

• GeneralMills/pytrends

  Pseudo API for Google Trends

  Language:Python3.3k826

• ckiplab/ckiptagger

  CKIP Neural Chinese Word Segmentation, POS Tagging, and NER

  Language:Python1.6k194

• fxsjy/jieba

  结巴中文分词

  Language:Python33.5k6.7k

• leon20121005/DelicacySentimentAnalysis

  Language:Java41

• ntunlplab/Finance-NTUSD-Fin

  NTUSD-Fin provides various scoring methods including frequency, CFIDF, chi-squared value, market sentiment score and word vector for the tokens. Only the tokens appeared at least ten times and shown significantly difference between expected and observed frequency with chi-squared test are remained in our dictionary. The predetermined significance level is 0.05. The market sentiment score is calculated by substracting the bearish PMI from the bullish PMI. There are 8,331 words, 112 hashtags and 115 emojis in the constructed dictionary, NTUSD-Fin.

  52

• ntunlplab/FrameNet-CFN-Lex

  A total of 36K lexical units that cover 779 frames for FrameNet in Chinese. This resource is extracted from a large-scale bilingual corpus to achieve higher coverage in terms of lexical units, which is helpful in providing frame recommendations for annotation campaigns or constructing robust frame identification systems.

  41

• ntunlplab/Finance-FinProLex

  FinProLex provides 5,162 tokens in professional analysts' reports and the financial social media platform posts with expert-like scores. The expert-like scores are calculated based on the pointwise mutual information (PMI).

  41

• shirosaidev/stocksight

  Stock market analyzer and predictor using Elasticsearch, Twitter, News headlines and Python natural language processing and sentiment analysis

  Language:Python2.2k473

• jojowither/Taiwan-Stock-Knowledge-Graph

  A knowledge graph about Taiwan stock

  Language:Jupyter Notebook215