在情感分析中对代数方程的确定性解法
Maryam Jalali1, Morteza Zahedi1, Abdolali Basiri2
1Faculty of Computer and IT Engineering, Shahrood University of Technology, Shahrood, Iran.
概括
本研究介绍了一种快速,确定性的文本挖掘方法,使用提霍诺夫规范化进行情感分析. 这种方法比传统的机器学习和随机方法提高了准确性.
科学领域:
- 自然语言处理自然语言处理.
- 计算语言学 计算语言学
- 机器学习 机器学习
背景情况:
- 传统的文本挖掘通常依赖于需要大量训练数据的统计方法.
- 对于情绪分析等任务的随机方法可能是样本密集型和计算昂贵的.
- 对于复杂的自然语言处理 (NLP) 数据,现有的确定性方法可能缺乏效率.
研究的目的:
- 为NLP任务提出一个快速有效的确定性文本挖掘方法.
- 解决文字分析中随机和传统机器学习方法的局限性.
- 通过一种新的数学方法结合语义信息来增强情感分析.
主要方法:
- 将文本和标签转换成一个方程系统.
- 应用蒂霍诺夫规则化,这是一个数学解决不良方程的方法.
- 根据解决方案的平滑性来赋予情感词的权重,以捕捉语义意义.
主要成果:
- 在SemEval-2013,ESWC数据库和Taboada数据库中验证了该方法的效率.
- 与基线方法相比,观察到处理负极性的显著改善.
- 与常见的机器学习,随机和模糊技术相比,已经证明了更高的有效性.
结论:
- 提出的确定性方法为文本挖掘和情绪分析提供了快速有效的替代方案.
- 提霍诺夫规范化为从文本中提取语义信息提供了一个强大的数学框架.
- 该方法对提高NLP任务的性能,特别是情绪分析,显示出希望.
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