Sentip: Thai Social Data Sentiment Classifier

Bidirectional GRU-CNN with Self-Attention Model for Thai Social Data Sentiment Classifier

This project is the part of my Senior Project in CSS403 Computer Engineering Project, Computer Engineering Department at SIIT

My Advisor

• Assoc. Prof. Dr. Ekawit Nantajeewarawat
• Nuttapong Sanglerdsinlapachai

Requirements

• Python 3.7.x
• pip install -r requirements_dev.txt

How to use and Examples

• pip install Sentip
• Version 1.0.0
• Notebook Example

!pip install Sentip
from Sentip.Sentip import *

sentip = Sentip()
sentiment_result1 = sentip.sentiment('นี่มันแย่มากเลย')

print(sentiment_result1)
> ['neg']

input_text = ['สวัสดี','ที่สุดไปเลย']
sentiment_result2 = sentip.sentiment(input_text)

print(sentiment_result2)
> ['neu', 'pos']

Limitation

• Must feeding with String | List | Numpy-Array
• Max Word for feeding: 450 words / paragraph
• Results: [sentiment1, sentiment2, sentiment3,..., sentiment4]

Dependency

• Tokenization apply pythainlp.tokenize.word_tokenize(_text_ls, engine="newmm")
• POS apply pythainlp.tag.pos_tag(_sentence_ls, corpus="orchid")
• Data provided by Wisesight Sentiment Corpus
• Word Embedding provided by thai2fit

Contributors

• Panjapol Ampornratana
• Phitchayapha Niyomwan
• Natnapin Maspakorn