Update README.md
Browse files
README.md
CHANGED
|
@@ -80,106 +80,14 @@ Tachygraphy—originally developed to expedite writing—has evolved over centur
|
|
| 80 |
|
| 81 |
|
| 82 |
### Sample Example 1
|
| 83 |
-
```mermaid
|
| 84 |
-
graph TD;
|
| 85 |
-
%% Input and normalized text nodes
|
| 86 |
-
A["Input Text: i don't know for real y he's sooo sad"]
|
| 87 |
-
B["Normalized Text: i do not know for real why he's so sad"]
|
| 88 |
-
C["Sentiment"]
|
| 89 |
-
|
| 90 |
-
A --> B
|
| 91 |
-
A -->|Sentiment| C
|
| 92 |
-
|
| 93 |
-
%% Sentiment value nodes (values inside the boxes)
|
| 94 |
-
C -->|Negative| D["0.99587"]
|
| 95 |
-
C -->|Neutral| E["6.23e-05"]
|
| 96 |
-
C -->|Positive| F["2.10e-05"]
|
| 97 |
-
|
| 98 |
-
%% Converge sentiment nodes to Emotion stage
|
| 99 |
-
D -->|Emotion| G
|
| 100 |
-
E -->|Emotion| G
|
| 101 |
-
F -->|Emotion| G
|
| 102 |
-
|
| 103 |
-
G["Emotion"]
|
| 104 |
-
|
| 105 |
-
%% Emotion nodes: arrow labels show emotion category; node boxes show numeric values.
|
| 106 |
-
G -->|Anger| H["0.0"]
|
| 107 |
-
G -->|Disgust| I["0.0"]
|
| 108 |
-
G -->|Fear| J["0.01028"]
|
| 109 |
-
G -->|Joy| K["0.0"]
|
| 110 |
-
G -->|Neutral| L["0.02194"]
|
| 111 |
-
G -->|Sadness| M["1.0"]
|
| 112 |
-
G -->|Surprise| N["0.02158"]
|
| 113 |
-
|
| 114 |
-
```
|
| 115 |
|
| 116 |
-
|
| 117 |
-
```mermaid
|
| 118 |
-
graph LR;
|
| 119 |
-
%% Input and normalized text nodes
|
| 120 |
-
A["Input Text: you rlly think all that talk means u tough? lol, when I step up, u ain't gon say sh*t"]
|
| 121 |
-
B["Normalized Text: you really think all that talk makes you tough [lol](laughed out loud) when i step up you are not going to say anything"]
|
| 122 |
-
C["Sentiment"]
|
| 123 |
-
|
| 124 |
-
A --> B
|
| 125 |
-
A -->|Sentiment| C
|
| 126 |
-
|
| 127 |
-
%% Sentiment value nodes
|
| 128 |
-
C -->|Negative| D["0.99999"]
|
| 129 |
-
C -->|Neutral| E["6.89e-06"]
|
| 130 |
-
C -->|Positive| F["1.11e-05"]
|
| 131 |
-
|
| 132 |
-
%% Converge sentiment nodes to Emotion stage
|
| 133 |
-
D -->|Emotion| G
|
| 134 |
-
E -->|Emotion| G
|
| 135 |
-
F -->|Emotion| G
|
| 136 |
-
|
| 137 |
-
G["Emotion"]
|
| 138 |
-
|
| 139 |
-
%% Emotion nodes: arrow labels show emotion category; nodes show numeric values.
|
| 140 |
-
G -->|Anger| H["0.14403"]
|
| 141 |
-
G -->|Disgust| I["0.03928"]
|
| 142 |
-
G -->|Fear| J["0.01435"]
|
| 143 |
-
G -->|Joy| K["0.04897"]
|
| 144 |
-
G -->|Neutral| L["0.49485"]
|
| 145 |
-
G -->|Sadness| M["0.02111"]
|
| 146 |
-
G -->|Surprise| N["0.23741"]
|
| 147 |
-
|
| 148 |
-
```
|
| 149 |
-
|
| 150 |
-
### Sample Example 3
|
| 151 |
-
```mermaid
|
| 152 |
-
graph TD;
|
| 153 |
-
%% Input and normalized text nodes
|
| 154 |
-
A["Input Text: bruh, floods in Kerala, rescue ops non‑stop 🚁"]
|
| 155 |
-
B["Normalized Text: Brother, the floods in Kerala are severe, and rescue operations are ongoing continuously."]
|
| 156 |
-
C["Sentiment"]
|
| 157 |
-
|
| 158 |
-
A --> B
|
| 159 |
-
A -->|Sentiment| C
|
| 160 |
-
|
| 161 |
-
%% Sentiment value nodes
|
| 162 |
-
C -->|Negative| D["4.44e-05"]
|
| 163 |
-
C -->|Neutral| E["0.99989"]
|
| 164 |
-
C -->|Positive| F["7.10e-05"]
|
| 165 |
|
| 166 |
-
|
| 167 |
-
D -->|Emotion| G
|
| 168 |
-
E -->|Emotion| G
|
| 169 |
-
F -->|Emotion| G
|
| 170 |
-
|
| 171 |
-
G["Emotion"]
|
| 172 |
-
|
| 173 |
-
%% Emotion nodes: arrow labels show emotion category; node boxes show numeric values.
|
| 174 |
-
G -->|Anger| H["0.08018"]
|
| 175 |
-
G -->|Disgust| I["0.01526"]
|
| 176 |
-
G -->|Fear| J["0.60187"]
|
| 177 |
-
G -->|Joy| K["0.00411"]
|
| 178 |
-
G -->|Neutral| L["0.02194"]
|
| 179 |
-
G -->|Sadness| M["1.0"]
|
| 180 |
-
G -->|Surprise| N["0.02158"]
|
| 181 |
|
|
|
|
| 182 |
|
|
|
|
| 183 |
|
| 184 |
-
|
| 185 |
|
|
|
|
| 80 |
|
| 81 |
|
| 82 |
### Sample Example 1
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 83 |
|
| 84 |
+

|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 85 |
|
| 86 |
+
### Sample Example 2
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 87 |
|
| 88 |
+

|
| 89 |
|
| 90 |
+
### Sample Example 3
|
| 91 |
|
| 92 |
+

|
| 93 |
|