帶有預定義文本的情感分析

Question

我正在使用NLTK在Python中進行情感分析項目。 項目的輸出必須顯示給定的陳述是肯定的還是負面的。 我已經成功地做到了，但是如何獲得中立聲明的輸出呢？ 是否可以百分比形式輸出（即正百分比，負百分比或中性百分比）？

classifier.py

import random
import preprocess
import nltk

def get_classifier():
    data = preprocess.get_data()
    random.shuffle(data)

    split = int(0.8 * len(data))

    train_set = data[:split]
    test_set =  data[split:]

    classifier = nltk.NaiveBayesClassifier.train(train_set)

    accuracy = nltk.classify.util.accuracy(classifier, test_set)
    print("Generated Classifier")
    print('-'*70)
    print("Accuracy: ", accuracy)
    return classifier

preprocess.py

import nltk.classify
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize

stop_words = stopwords.words("english")

def create_word_features_pos(words):
    useful_words = [word for word in words if word not in stop_words]
    my_list = [({word: True}, 'positive') for word in useful_words]
    return my_list

def create_word_features_neg(words):
    useful_words = [word for word in words if word not in stop_words]
    my_list = [({word: True}, 'negative') for word in useful_words]
    return my_list

def create_word_features(words):
    useful_words = [word for word in words if word not in stopwords.words("english")]

    pos_txt = get_tokenized_file(u"positive-words.txt")
    neg_txt = get_tokenized_file(u"negative-words.txt")

    my_dict = dict([(word, True) for word in pos_txt if word in useful_words])
    my_dict1 = dict([(word, False) for word in neg_txt if word in useful_words])
    my_dict3 = dict([word,])
    my_dict.update(my_dict1)

    return my_dict

def get_tokenized_file(file):
    return word_tokenize(open(file, 'r').read())

def get_data():
    print("Collecting Negative Words")
    neg_txt = get_tokenized_file(u"negative-words.txt")
    neg_features = create_word_features_neg(neg_txt)

    print("Collecting Positive Words")
    pos_txt = get_tokenized_file(u"positive-words.txt")
    pos_features = create_word_features_pos(pos_txt)
    return pos_features + neg_features

def process(data):
    return [word.lower() for word in word_tokenize(data)]

Answer 1

nltk.NaiveBayesClassifier.train的文檔：

參數：labeled_featuresets-分類特征集的列表，即元組列表（特征集，標簽）。

這意味着您的train_set是一組(features, label)的元組。

如果要添加neutral類型，則需要將某些數據標記為neutral否則分類器將無法學習該新類型。

現在，您將數據標記為： (word, True)和(word, False) ，切換到3個標簽的示例是(word, 0) ， (word, 1) ， (word, 2)

nltk.NaiveBayesClassifier.prob_classify將返回每個標簽的概率。

可以在這里找到文檔： https : //www.nltk.org/api/nltk.classify.html#nltk.classify.naivebayes.NaiveBayesClassifier