将自由文本语句与预定义属性相关联 [英] Associating free text statements with pre-defined attributes
问题描述
我列出了人们关注的数十种产品属性，例如
I have a list of several dozen product attributes that people are concerned with, like
- 融资
- 制造质量
- 耐久性
- 销售经验
以及客户关于该产品的数百万条自由文本声明，例如
and several million free-text statements from customers about the product, e.g.
融资很容易，但房屋脆弱."
"The financing was easy but the housing is flimsy."
我想对每个自由文本语句与每个属性的关联程度以及它是正向还是负向进行评分.
I would like to score each free text statement in terms of how strongly it relates to each of the attributes, and whether that is a positive or negative association.
在给定的示例中，与Financing的关联性很强，与Manufacturing quality的关联性很强.
In the given example, there would be a strong positive association to Financing and a strong negative association to Manufacturing quality.
感觉这种类型的问题可能是自然语言编程(NLP)的领域.但是，我花了几个小时来阅读OpenNLP和NLTK之类的东西，发现有太多特定于领域的术语，以至于我无法弄清楚该解决特定问题的重点.
It feels like this type of problem is probably the realm of Natural Language Programming (NLP). However, I spent several hours reading up on things like OpenNLP and NLTK and find there's so much domain specific terminology that I cannot figure out where to focus to solve this specific problem.
所以我的问题分为三部分:
So my three-part question:
- NLP是解决此类问题的正确途径吗?
- 我应该重点学习NLP的哪个方面?
- 有没有我没有考虑过的替代方案?
推荐答案
Yes, this is a NLP problem by the name of Sentiment analysis. Sentiment analysis is an active research area with different approaches and a task where a lot of other NLP-methods have to work together, so it is certainly not the easiest field to get started with in NLP.
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