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ABSA_system

一个基础且完整的细粒度情感分析(ABSA,Aspect based sentiment analysis)案例,基于semeval-2014 ABSA课题

相关博客地址:https://blog.csdn.net/HapHapYear/article/details/101102870

数据与工具

SemEval 2014 ABSA 竞赛数据:https://pan.baidu.com/s/18fsF8Bx4yZxpw9gZy2u9Fg 原始数据地址:http://alt.qcri.org/semeval2014/task4/index.php?id=data-and-tools

yelp 餐厅评论语料:https://pan.baidu.com/s/12h7xCFgnlxZX2CWSlmhlBg 原始数据地址:https://www.yelp.com/dataset

amzon电子产品评论语料:https://pan.baidu.com/s/1emRDSdwWYNZ2RePboYq85w 原始数据地址: http://jmcauley.ucsd.edu/data/amazon/

stanford parser网盘备份(本文版本):https://pan.baidu.com/s/17xLGWRqHLvA827jpaQmhEQ 原始下载地址(最新):https://nlp.stanford.edu/software/lex-parser.shtml

注意项

  1. 在运行代码前,你需要安装如下重要的python库(pip安装即可) gensim, nltk, python-crfsuite,(其他sklearn,pandas什么的不列了,安个anaconda即可)
  2. 其中python-crfsuite为crf模型依赖库,导入用import pycrfsuite。
  3. nltk安好后并不能直接使用,需要在python里执行nltk.download()下载模型和语料,这是必须的,否则无法进行词性标注等任务。
  4. 代码有用到stanford parser,nltk可以调用其接口,但是工具需要自行下载(链接见顶上), 工程目录下有个空的stanford parser目录,将下载的stanford parser内容全部解压到该目录即可。
  • baseline文件夹下为semeval-2014的官方baseline
  • boot.py为程序流程的入口

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