使用 VADER 进行情感分析

2024 年 8 月 29 日 | 阅读 3 分钟

情感分析是一个‘计算确定’一段文本是积极、中性还是消极的过程。它也被称为意见挖掘。这个过程确定段落中的句子是消极、积极还是中性。

商业：营销公司利用它来制定策略，了解客户对产品和品牌的感受，他们如何响应营销活动和产品发布，以及为什么一些消费者不购买某些产品。
政治：它被用于政治领域，以追踪政治观点，并在政府层面检测言行之间的一致性或不一致性。它还可以用于预测选举结果。
公众活动：情感分析也用于分析社会现象，并发现潜在的危险情况。

安装 vaderSentiment

我们可以使用以下命令安装 vaderSentiment 模块

VADER 情感分析

Valence Aware Dictionary and Sentiment Reasoner (VADER) 是一种字典，它使用基于规则的情感分析工具。它尤其适用于社交媒体情感。VADER 使用情感词典的混合，该词典是语言特征（例如，单词）的列表，这些语言特征根据其语义方向被标记为消极或积极。VADER 不仅告诉我们积极性或消极性的得分，还告诉我们情感有多积极和多消极。

代码

# First, we will import SentimentIntensityAnalyzer class.

from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer as SIA

# Now, we will create a function for printing sentiments of the sentence.
def sentiment_scores1(sentence):

	# Here, we will create a SIA object.
	sid_obj1 = SIA()

	# Now, we will create polarity_scores method of SIA object 
    #gives a sentiment dictionary. which contains positive (pos), negative (neg), # neutral (neu), and compound scores.
	sentiment_dict1 = sid_obj1.polarity_scores(sentence)
	
	print ("The overall sentiment dictionary is: ", sentiment_dict1)
	print ("The sentence has been rated as ", sentiment_dict1['neg'] * 100, "% Negative")
	print ("The sentence has been rated as ", sentiment_dict1['neu'] * 100, "% Neutral")
	print ("The sentence has been rated as ", sentiment_dict1['pos'] * 100, "% Positive")

	print("The sentence Overall Rated As ", end = " ")

	# Here, we will decide sentiment as positive (pos), negative(neg) and neutral(neu)
	if sentiment_dict1['compound'] >= 0.05 :
		print(" It is Positive")

	elif sentiment_dict1['compound'] <= - 0.05 :
		print("It is Negative")

	else :
		print("It is Neutral")



# Driver code
if __name__ == "__main__" :

	print("\n The 1st statement :")
	sentence = "Jack, Jill and Bill are living in America.\ Jack use to go for running every morning " 

	# Now, we will create a function calling
	sentiment_scores1(sentence)

	print("\n The 2nd Statement :")
	sentence = "jill is a school teacher."
	sentiment_scores1(sentence)

	print("\n The 3rd Statement :")
	sentence = "bill is a fun guy, for him laughing is the best medicine"
	sentiment_scores1(sentence)

输出

The 1st statement :
The overall sentiment dictionary is:  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}
The sentence has been rated as  0.0 % Negative
The sentence has been rated as  100.0 % Neutral
The sentence has been rated as  0.0 % Positive
The sentence Overall Rated As  It is Neutral

 The 2nd Statement :
The overall sentiment dictionary is:  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}
The sentence has been rated as  0.0 % Negative
The sentence has been rated as  100.0 % Neutral
The sentence has been rated as  0.0 % Positive
The sentence Overall Rated As  It is Neutral

 The 3rd Statement :
The overall sentiment dictionary is:  {'neg': 0.0, 'neu': 0.457, 'pos': 0.543, 'compound': 0.8934}
The sentence has been rated as  0.0 % Negative
The sentence has been rated as  45.7 % Neutral
The sentence has been rated as  54.300000000000004 % Positive
The sentence Overall Rated As   It is Positive

复合得分是通过对所有词典评分进行求和而计算出来的，这些评分在 -1（最极端消极）或 +1（最极端积极）之间进行了归一化。

积极情感：(复合得分 > = 0.0.05)；

中性情感：(复合得分 > -0.05)；

消极情感：(复合得分 = 0.0.05)；。

下一个主题Python 的 break 语句

← 上一个下一个 →

使用 VADER 进行情感分析

VADER 情感分析

联系信息

关注我们

教程

面试题

在线编译器

Python

Java

.Net Framework

AI, ML and Data Science

Cloud Technology

B.Tech and MCA

Web Technology

PHP

Software Testing

Technical Interview

Java Interview

Python

Web Interview

Database Interview

B.Tech / MCA

Important Interview

Software Testing Interview

Company Interviews

Online Compilers

Multiple Choice Questions

Python 问题

使用 VADER 进行情感分析

VADER 情感分析

相关帖子

Python Web2py 框架

如何在 Python 中验证电子邮件

Python 方差函数

Python PyGal 库

Python 中不使用 itertools 的组合

Python 程序计算买卖股票的最佳时机

如何在 Python 中获取当前日期

使用 Flask、Connexion 和 SQLAlchemy 构建 Python REST API

如何将列表项分配给字典

如何使用 Python 清空回收站

订阅 Tpoint Tech

联系信息

关注我们

教程

面试题

在线编译器