无监督情绪分析

问题描述

我已经阅读了很多文章，这些文章解释了在情感分析系统真正起作用之前，需要将最初的一组文本分类为正"或负"的问题.

我的问题是:是否有人尝试对正"形容词和负"形容词进行基本检查，并考虑到任何简单的否定词，以避免将不快乐"归类为正?如果是这样，有没有文章讨论为什么这种策略不现实?

解决方案

A 经典论文(2002)解释了一种仅使用 excellent 和差作为种子集. Turney在这两个形容词中使用了相互信息换句话说，达到了74％的准确性. /p>

I've been reading a lot of articles that explain the need for an initial set of texts that are classified as either 'positive' or 'negative' before a sentiment analysis system will really work.

My question is: Has anyone attempted just doing a rudimentary check of 'positive' adjectives vs 'negative' adjectives, taking into account any simple negators to avoid classing 'not happy' as positive? If so, are there any articles that discuss just why this strategy isn't realistic?

解决方案

A classic paper by Peter Turney (2002) explains a method to do unsupervised sentiment analysis (positive/negative classification) using only the words excellent and poor as a seed set. Turney uses the mutual information of other words with these two adjectives to achieve an accuracy of 74%.

这篇关于无监督情绪分析的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！