financial-machine-learning/generated_wiki/textual.md

| <sub>repo</sub>                                                                                                                                 | <sub>comment</sub>                                                                           | <sub>created_at</sub>    | <sub>last_commit</sub>   | <sub>star_count</sub>   | <sub>repo_status</sub>        | <sub>rating</sub>   |
|:------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------|:-------------------------|:-------------------------|:------------------------|:------------------------------|:--------------------|
| <sub>[NLP](https://github.com/toamitesh/NLPinFinance)</sub>                                                                                     | <sub>This project assembles a lot of NLP operations needed for finance domain.</sub>         | <sub>nan</sub>           | <sub>nan</sub>           | <sub>nan</sub>          | <sub>:heavy_check_mark:</sub> | <sub></sub>         |
| <sub>[Extensive NLP](https://github.com/TiesdeKok/Python_NLP_Tutorial/blob/master/NLP_Notebook.ipynb)</sub>                                     | <sub>Comprehensive NLP techniques for accounting research.</sub>                             | <sub>10/25/17 7:10</sub> | <sub>6/5/20 3:28</sub>   | <sub>73.0</sub>         | <sub>:heavy_check_mark:</sub> | <sub></sub>         |
| <sub>[Financial Statement Sentiment](https://github.com/MAydogdu/TextualAnalysis)</sub>                                                         | <sub>Extracting sentiment from financial statements using neural networks.</sub>             | <sub>6/4/18 20:54</sub>  | <sub>6/4/18 20:56</sub>  | <sub>7.0</sub>          | <sub>:heavy_check_mark:</sub> | <sub></sub>         |
| <sub>[NLP Event](https://github.com/yuriak/DLQuant)</sub>                                                                                       | <sub>Applying Deep Learning and NLP in Quantitative Trading.</sub>                           | <sub>7/2/18 23:50</sub>  | <sub>1/31/19 14:08</sub> | <sub>68.0</sub>         | <sub>:heavy_check_mark:</sub> | <sub></sub>         |
| <sub>[Financial Sentiment Analysis](https://github.com/EricHe98/Financial-Statements-Text-Analysis)</sub>                                       | <sub>Sentiment, distance and proportion analysis for trading signals.</sub>                  | <sub>6/23/17 0:05</sub>  | <sub>1/26/19 3:35</sub>  | <sub>47.0</sub>         | <sub>:heavy_check_mark:</sub> | <sub></sub>         |
| <sub>[Fund classification](https://github.com/frechfrechfrech/Mutual-Fund-Market-Clusters/blob/master/Initial%20Data%20Exploration.ipynb)</sub> | <sub>Fund classification using text mining and NLP.</sub>                                    | <sub>4/16/18 22:18</sub> | <sub>6/7/18 22:01</sub>  | <sub>3.0</sub>          | <sub>:heavy_check_mark:</sub> | <sub></sub>         |
| <sub>[Earning call transcripts](https://github.com/lin882/WebAnalyticsProject)</sub>                                                            | <sub>Correlation between mutual fund investment decision and earning call transcripts.</sub> | <sub>12/30/17 8:56</sub> | <sub>1/11/18 2:11</sub>  | <sub>3.0</sub>          | <sub>:heavy_check_mark:</sub> | <sub></sub>         |
| <sub>[Accounting Anomalies](https://github.com/GitiHubi/deepAI/blob/master/GTC_2018_Lab-solutions.ipynb)</sub>                                  | <sub>Using deep-learning frameworks to identify accounting anomalies.</sub>                  | <sub>5/24/17 12:36</sub> | <sub>8/7/19 21:47</sub>  | <sub>106.0</sub>        | <sub>:heavy_check_mark:</sub> | <sub></sub>         |
| <sub>[Buzzwords](https://github.com/swap9047/Cutting-Edge-Technologies-Effect-on-S-P500-Companies-Performance-and-Mutual-Funds)</sub>           | <sub>Return performance and mutual fund selection.</sub>                                     | <sub>2/4/18 21:51</sub>  | <sub>2/4/18 21:57</sub>  | <sub>1.0</sub>          | <sub>:heavy_check_mark:</sub> | <sub></sub>         |