情感分析 - wordNet , sentiWordNet 词典 [英] sentiment analysis - wordNet , sentiWordNet lexicon

问题描述

我需要一个正面和负面词的列表，并根据词的强度和周数为词分配权重.我有:

I need a list of positive and negative words with the weights assigned to words according to how strong and week they are. I have got :

1.) WordNet - 它为每个单词提供 + 或 - 分数.

1.) WordNet - It gives a + or - score for every word.

2.) SentiWordNet - 给出 [0,1] 范围内的正值和负值.

2.) SentiWordNet - Giving positive and negative values in the range [0,1].

我检查了几个字，

love - wordNet 对名词和动词都给出了 0.0，我不知道为什么我认为它至少在某些因素上应该是积极的.

love - wordNet is giving 0.0 for both noun and verb, I dont know why i think it should be positive by at least some factor.

压制 - wordNet 给出 -9.93
- SentiWordNet 为 pos 和 neg 提供 - 0.0.(应该是负数)

repress - wordNet gives -9.93
- SentiWordNet gives - 0.0 for both pos and neg. (should be negative)

休息 - wordNet - 2.488
- SentiWordNet - { pos - 0.125, neg - 0.5 }(应该是正数)

repose - wordNet - 2.488
- SentiWordNet - { pos - 0.125, neg - 0.5 } (should be positive)

我需要一些帮助来决定使用哪一个.

I need some help to decide which one to use.

谢谢.

推荐答案

通常程度和/或极性可能取决于域和/或上下文，因此仅凭这个词并不足以做出决定.

Quite often the degree and/or polarity may depend on the domain and/or the context, so the word alone isn't really enough to make a decision.

如果您有一些带注释的数据，我建议使用两种资源提供的分数作为特征来训练分类器.如果不这样做，一种选择是使用与相关域匹配的可用情感注释语料库之一.没有任何数据，整个任务变得有些棘手，尽管在情感分类的无监督方法上有大量工作，我相信，例如无监督情绪分析

If you have some annotated data, I suggest training a classifier on that using the scores provided by the two resources as features. If you don't, one option is to use one of the available sentiment-annotated corpora that matches the domain in question. Without any data at all the whole task becomes somewhat tricky, although there is a substantial body of work on unsupervised approaches to sentiment classification, I believe, see, e.g. Unsupervised Sentiment Analysis

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