The (extremely) naive sentiment classification function based on NBSVM trained on wisesight_sentiment

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Text Data & NLPthai_sentiment
Overview

thai_sentiment

The naive sentiment classification function based on NBSVM trained on wisesight_sentiment

วิธีติดตั้ง

pip install thai_sentiment==0.1.3

วิธีใช้

from thai_sentiment import get_sentiment

get_sentiment('ร้านนี้บรรยากาศดีนะครับ')
> ('pos', #pos แปลว่า เป็นบวก
 {'pos': 0.33864993351079425,
  'neu': 0.28699790627796756,
  'neg': 0.07578396636250984})
  
get_sentiment('ร้านนี้อาหารอย่างแย่')
> ('neg', #neg แปลว่า เป็นลบ
 {'pos': 0.07848318054147058,
  'neu': 0.28609131356977374,
  'neg': 0.3279735800256706})
  
get_sentiment('ร้านนี้อยู่บางรัก')
> ('neu', #neu แปลว่า เฉยๆ
 {'pos': 0.23328174158421325,
  'neu': 0.6859672540205807,
  'neg': 0.024412368023402797})

Notes

Library นี้สร้างขึ้นจากการใช้โมเดล NBSVM ที่เทรนด้วยข้อมูล wisesight_sentiment ซึ่งจะเห็นว่าได้ผลดีพอประมาณ (Micro-averaged F1 72.03 vs 76.19 จาก WangchanBERTa โมเดลที่ดีที่สุด เหมาะสำหรับคนที่ไม่อยากเทรนโมเดลอะไรเลย แค่อยากเรียก function get_sentiment แล้วได้ว่าข้อความเป็น บวก, กลาง, ลบ

Micro-averaged F1
NBSVM 72.03
ULMFit 70.95
XLMR 73.57
mBERT 70.05
WanchanBERTa 76.19

แน่นอนว่าโมเดล machine learning ทุกโมเดลทำงานได้ดีใน domain เฉพาะของมัน เพราะงั้นถ้าคุณอยากได้โมเดลดีๆเราก็แนะนำให้ไปเทรนโมเดลบนชุดข้อมูลของคุณเองตามโพสนี้มากกว่าที่จะมานั่งเรียก function ที่เทรนจากชุดข้อมูลอื่นแบบนี้

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