使用 Pandas 数据框进行 Spacy 依赖解析

如何解决使用 Pandas 数据框进行 Spacy 依赖解析

我想在我的 Pandas 数据帧上使用 Spacy 的依赖解析器为基于方面的情感分析提取名词-形容词对。我在来自 kaggle 的亚马逊美食评论数据集上尝试使用此代码：Named Entity Recognition in aspect-opinion extraction using dependency rule matching

但是，我将熊猫数据框提供给 spacy 的方式似乎有问题。我的结果不是我期望的那样。有人可以帮我调试这个吗？非常感谢。

!python -m spacy download en_core_web_lg
import nltk
nltk.download('vader_lexicon')

import spacy
nlp = spacy.load("en_core_web_lg")

from nltk.sentiment.vader import SentimentIntensityAnalyzer
sid = SentimentIntensityAnalyzer()


def find_sentiment(doc):
    # find roots of all entities in the text
  for i in df['Text'].tolist():
    doc = nlp(i)
    ner_heads = {ent.root.idx: ent for ent in doc.ents}
    rule3_pairs = []
    for token in doc:
        children = token.children
        A = "999999"
        M = "999999"
        add_neg_pfx = False
        for child in children:
            if(child.dep_ == "nsubj" and not child.is_stop): # nsubj is nominal subject
                if child.idx in ner_heads:
                    A = ner_heads[child.idx].text
                else:
                    A = child.text
            if(child.dep_ == "acomp" and not child.is_stop): # acomp is adjectival complement
                M = child.text
            # example - 'this Could have been better' -> (this,not better)
            if(child.dep_ == "aux" and child.tag_ == "MD"): # MD is modal auxiliary
                neg_prefix = "not"
                add_neg_pfx = True
            if(child.dep_ == "neg"): # neg is negation
                neg_prefix = child.text
                add_neg_pfx = True
        if (add_neg_pfx and M != "999999"):
            M = neg_prefix + " " + M
        if(A != "999999" and M != "999999"):
            rule3_pairs.append((A,M,sid.polarity_scores(M)['compound']))
    return rule3_pairs
df['three_tuples'] = df['Text'].apply(find_sentiment) 
df.head()

我的结果是这样的，这显然意味着我的循环有问题：

解决方法

如果您在 apply 上调用 df['Text']，那么您实际上是在遍历该列中的每个值并将该值传递给函数。

然而，在这里，您的函数本身会迭代您正在应用该函数的同一数据帧列，同时还会覆盖在函数早期传递给它的值。

所以我会从如下重写函数开始，看看它是否产生了预期的结果。我不能肯定，因为您没有发布任何示例数据，但这至少应该使球向前移动：

def find_sentiment(text):
    doc = nlp(text)
    ner_heads = {ent.root.idx: ent for ent in doc.ents}
    rule3_pairs = []
    for token in doc:
        children = token.children
        A = "999999"
        M = "999999"
        add_neg_pfx = False
        for child in children:
            if(child.dep_ == "nsubj" and not child.is_stop): # nsubj is nominal subject
                if child.idx in ner_heads:
                    A = ner_heads[child.idx].text
                else:
                    A = child.text
            if(child.dep_ == "acomp" and not child.is_stop): # acomp is adjectival complement
                M = child.text
            # example - 'this could have been better' -> (this,not better)
            if(child.dep_ == "aux" and child.tag_ == "MD"): # MD is modal auxiliary
                neg_prefix = "not"
                add_neg_pfx = True
            if(child.dep_ == "neg"): # neg is negation
                neg_prefix = child.text
                add_neg_pfx = True
        if (add_neg_pfx and M != "999999"):
            M = neg_prefix + " " + M
        if(A != "999999" and M != "999999"):
            rule3_pairs.append((A,M,sid.polarity_scores(M)['compound']))
    return rule3_pairs