# KeyMoji [Emotions in Formula]
KeyMoji 關鍵情緒偵測 (**SENSE2、SENSE8、Tension**) 採用不同於其它「素人標記」和「純機器學習」的文本情緒偵測分析工具，結合了「**句型**」、「**邏輯語意**」和「**詞彙模型**」，設計出一個完整的「情緒計算過程」。
[完整介紹](https://api.droidtown.co/document/#KeyMoji)
## 安裝方法
```sh
pip3 install KeyMojiAPI
```
or
```sh
python3 -m pip install KeyMojiAPI
```
## 使用方法
[KeyMoji Website Demo](https://api.droidtown.co/#keymoji)
[KeyMojiAPI Documentation](https://api.droidtown.co/KeyMojiAPI/document/)
### SENSE2
```python
from KeyMojiAPI import KeyMoji
# 若您是使用 Docker 版本，無須填入 username, keymoji_key 參數
keymoji = KeyMoji(username="", keymojiKey="")
inputSTR = "他逃離了危險的災難"
result = keymoji.sense2(inputSTR)
print(result)
```
```json
{
"status": true,
"msg": "Success!",
"results": [
{
"score": 0.2798,
"sentiment": "positive",
"input_str": "他逃離了危險的災難",
"cursing": false
}
],
"sense": "sense2",
"version": "v101"
}
```
### Visualization
### SENSE8
```python
from KeyMojiAPI import KeyMoji
# 若您是使用 Docker 版本，無須填入 username, keymoji_key 參數
keymoji = KeyMoji(username="", keymojiKey="")
inputSTR = "他逃離了危險的災難"
result = keymoji.sense8(inputSTR)
print(result)
```
```json
{
"status": true,
"msg": "Success!",
"results": [
{
"input_str": "他逃離了危險的災難",
"Joy": 3.7486,
"Trust": 5.1776,
"Surprise": 6.7238,
"Anticipation": 0.9618,
"Fear": 0.9505,
"Sadness": 0.9108,
"Anger": 0.9516,
"Disgust": 0.8876
}
],
"sense": "sense8",
"version": "v101"
}
```
### Visualization
### Tension Visualization
Raw data
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"description": "# KeyMoji [Emotions in Formula]\n\nKeyMoji \u95dc\u9375\u60c5\u7dd2\u5075\u6e2c (**SENSE2\u3001SENSE8\u3001Tension**) \u63a1\u7528\u4e0d\u540c\u65bc\u5176\u5b83\u300c\u7d20\u4eba\u6a19\u8a18\u300d\u548c\u300c\u7d14\u6a5f\u5668\u5b78\u7fd2\u300d\u7684\u6587\u672c\u60c5\u7dd2\u5075\u6e2c\u5206\u6790\u5de5\u5177\uff0c\u7d50\u5408\u4e86\u300c**\u53e5\u578b**\u300d\u3001\u300c**\u908f\u8f2f\u8a9e\u610f**\u300d\u548c\u300c**\u8a5e\u5f59\u6a21\u578b**\u300d\uff0c\u8a2d\u8a08\u51fa\u4e00\u500b\u5b8c\u6574\u7684\u300c\u60c5\u7dd2\u8a08\u7b97\u904e\u7a0b\u300d\u3002\n\n[\u5b8c\u6574\u4ecb\u7d39](https://api.droidtown.co/document/#KeyMoji)\n\n## \u5b89\u88dd\u65b9\u6cd5\n\n```sh\npip3 install KeyMojiAPI\n```\nor\n```sh\npython3 -m pip install KeyMojiAPI\n```\n\n## \u4f7f\u7528\u65b9\u6cd5\n[KeyMoji Website Demo](https://api.droidtown.co/#keymoji) \n[KeyMojiAPI Documentation](https://api.droidtown.co/KeyMojiAPI/document/)\n\n### SENSE2\n```python\nfrom KeyMojiAPI import KeyMoji\n# \u82e5\u60a8\u662f\u4f7f\u7528 Docker \u7248\u672c\uff0c\u7121\u9808\u586b\u5165 username, keymoji_key \u53c3\u6578\nkeymoji = KeyMoji(username=\"\", keymojiKey=\"\")\n\ninputSTR = \"\u4ed6\u9003\u96e2\u4e86\u5371\u96aa\u7684\u707d\u96e3\"\nresult = keymoji.sense2(inputSTR)\nprint(result)\n```\n\n```json\n{\n \"status\": true,\n \"msg\": \"Success!\",\n \"results\": [\n {\n \"score\": 0.2798,\n \"sentiment\": \"positive\",\n \"input_str\": \"\u4ed6\u9003\u96e2\u4e86\u5371\u96aa\u7684\u707d\u96e3\",\n \"cursing\": false\n }\n ],\n \"sense\": \"sense2\",\n \"version\": \"v101\"\n}\n```\n\n### Visualization\n\n\n\n### SENSE8\n```python\nfrom KeyMojiAPI import KeyMoji\n# \u82e5\u60a8\u662f\u4f7f\u7528 Docker \u7248\u672c\uff0c\u7121\u9808\u586b\u5165 username, keymoji_key \u53c3\u6578\nkeymoji = KeyMoji(username=\"\", keymojiKey=\"\")\n\ninputSTR = \"\u4ed6\u9003\u96e2\u4e86\u5371\u96aa\u7684\u707d\u96e3\"\nresult = keymoji.sense8(inputSTR)\nprint(result)\n```\n\n```json\n{\n \"status\": true,\n \"msg\": \"Success!\",\n \"results\": [\n {\n \"input_str\": \"\u4ed6\u9003\u96e2\u4e86\u5371\u96aa\u7684\u707d\u96e3\",\n \"Joy\": 3.7486,\n \"Trust\": 5.1776,\n \"Surprise\": 6.7238,\n \"Anticipation\": 0.9618,\n \"Fear\": 0.9505,\n \"Sadness\": 0.9108,\n \"Anger\": 0.9516,\n \"Disgust\": 0.8876\n }\n ],\n \"sense\": \"sense8\",\n \"version\": \"v101\"\n}\n```\n\n### Visualization\n\n\n\n\n### Tension Visualization\n\n\n",
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"license": "MIT License",
"summary": "KeyMoji sentimental analysis system differs from other \"amature-tagged\" and \"pure-ML/DL\" text sentimental analysis solutions in many ways. KeyMoji combines \"syntactic structure information\", \"formal semantics\" and \"lexical semantic model\" in one calculating process to estimate the sentimental bias and distributions of sentiments in texts.",
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