senti_c (sentiment analysis toolkit for traditional Chinese)
簡介
本工具為繁體中文情感分析套件，支援三種類型分析：句子情感分類、屬性術語提取、屬性情感分類；同時提供函數供使用者應用其它資料重新微調模型。
目錄
執行環境
- python3.8
安裝方式
- 要先至 https://pytorch.org/ 下載適合作業系統的PyTorch Version 1.x套件。
1.pip
pip install senti_c
2.from source
git clone https://github.com/hsinmin/senti_c
cd senti_c
python3 setup.py install
功能介紹
1.句子情感分類：預測
from senti_c import SentenceSentimentClassification
sentence_classifier = SentenceSentimentClassification()
test_data = ["我很喜歡這家店！超級無敵棒！","這個服務生很不親切..."]
result = sentence_classifier.predict(test_data,run_split=True,aggregate_strategy=False) # 可依據需求調整參數
- 結果如下：
2.句子情感分類：重新微調模型
from senti_c import SentenceSentimentModel
sentence_classifier = SentenceSentimentModel()
sentence_classifier.train(data_dir="./data/sentence_data",output_dir="test_fine_tuning_sent") # 可依據需求調整參數
3.屬性情感分析：預測
from senti_c import AspectSentimentAnalysis
aspect_classifier = AspectSentimentAnalysis()
test_data = ["我很喜歡這家店！超級無敵棒！","這個服務生很不親切..."]
result = aspect_classifier.predict(test_data,output_result="all") # 可依據需求調整參數
- 結果如下：
4.屬性情感分析：重新微調模型
from senti_c import AspectSentimentModel
aspect_classifier = AspectSentimentModel()
aspect_classifier.train(data_dir="./data/aspect_data",output_dir="test_fine_tuning_aspect") # 可依據需求調整參數
範例程式
相關功能demo可參考examples資料夾中的function_demo檔案。
資料
本研究蒐集Google評論上餐廳與飯店領域評論內容、並進行句子情感分類、屬性情感分析標記 (屬性標記與情感標記)。
相關資料格式請見data資料夾。
引用
1.論文：
凃育婷（2020）。基於順序遷移學習開發繁體中文情感分析工具。國立臺灣大學資訊管理學研究所碩士論文，台北市。
2.實驗室：
Business Analytics and Economic Impact Research Lab
Department of Information Management
National Taiwan University
http://www.im.ntu.edu.tw/~lu/index.htm
致謝
本套件基於 Hugging Face 團隊開源的 transformers。
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