无监督情感分析 [英] Unsupervised Sentiment Analysis

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一组初始文本分为正面"或负面"的必要性.

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.

我的问题是:有没有人尝试过对积极"形容词与消极"形容词进行初步检查，并考虑到任何简单的否定词以避免将不快乐"归类为积极?如果是这样，是否有任何文章讨论为什么这种策略不现实?

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?

推荐答案

经典论文作者:Peter Turney (2002) 解释了一种仅使用单词 excellent 和 poor 作为种子集进行无监督情感分析(正面/负面分类)的方法.Turney 将其他词的相互信息与这两个形容词结合使用，达到了 74% 的准确率.

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%.

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