使用 Matplotlib 在 Python 中可视化情感分析结果-Python教程-PHP中文网

在本文中，我们将使用 Matplotlib 添加情感分析结果的图形表示。目标是可视化多个句子的情绪分数，并使用条形图使用不同的颜色区分积极和消极情绪。

先决条件

确保您安装了以下库：

pip install transformers torch matplotlib

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Transformer：用于处理预先训练的 NLP 模型。
torch：用于运行模型。
matplotlib：用于创建情感分析结果的图形表示。

可视化 Python 代码

Visualizing Sentiment Analysis Results in Python using Matplotlib

这是更新后的 Python 代码，它将情感分析与数据可视化相集成。

import matplotlib.pyplot as plt
from transformers import pipeline
from transformers import AutoTokenizer, AutoModelForSequenceClassification

# Load pre-trained model and tokenizer
model_name = "distilbert-base-uncased-finetuned-sst-2-english"
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Initialize the sentiment-analysis pipeline
classifier = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)

# List of 10 sentences for sentiment analysis
sentences = [
    "I love you! I love you! I love you!",
    "I feel so sad today.",
    "This is the best day ever!",
    "I can't stand the rain.",
    "Everything is going so well.",
    "I hate waiting in line.",
    "The weather is nice, but it's cold.",
    "I'm so proud of my achievements.",
    "I am very upset with the decision.",
    "I am feeling optimistic about the future."
]

# Prepare data for the chart
scores = []
colors = []

for sentence in sentences:
    result = classifier(sentence)
    sentiment = result[0]['label']
    score = result[0]['score']
    scores.append(score)

    # Color bars based on sentiment: Positive -> green, Negative -> red
    if sentiment == "POSITIVE":
        colors.append("green")
    else:
        colors.append("red")

# Create a bar chart
plt.figure(figsize=(10, 6))
bars = plt.bar(sentences, scores, color=colors)

# Add labels and title with a line break
plt.xlabel('Sentences')
plt.ylabel('Sentiment Score')
plt.title('Sentiment Analysis of 10 Sentences\n')  # Added newline here
plt.xticks(rotation=45, ha="right")

# Adjust spacing with top margin (to add ceiling space)
plt.subplots_adjust(top=0.85)  # Adjust the top spacing (20px roughly equivalent to 0.1 top margin)

plt.tight_layout()  # Adjusts the rest of the layout

# Display the sentiment score on top of the bars
for bar in bars:
    yval = bar.get_height()
    plt.text(bar.get_x() + bar.get_width() / 2, yval + 0.02, f'{yval:.2f}', ha='center', va='bottom', fontsize=9)

# Show the plot
plt.show()

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代码分解

导入必要的库：
我们导入 matplotlib.pyplot 来创建绘图和转换器来执行情感分析。

   import matplotlib.pyplot as plt
   from transformers import pipeline
   from transformers import AutoTokenizer, AutoModelForSequenceClassification

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加载预训练模型：
我们加载针对 SST-2 数据集进行情感分析而微调的 DistilBERT 模型。我们还加载关联的标记生成器，将文本转换为模型可读的标记。

   model_name = "distilbert-base-uncased-finetuned-sst-2-english"
   model = AutoModelForSequenceClassification.from_pretrained(model_name)
   tokenizer = AutoTokenizer.from_pretrained(model_name)

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初始化情感分析管道：
分类器管道是为情感分析而设置的。该管道负责对输入文本进行标记、执行推理并返回结果。

   classifier = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)

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情感分析句子：
我们创建一个包含 10 个句子的列表来进行分析。每句话都是一种独特的情感表达，从非常积极到消极。

   sentences = [
       "I love you! I love you! I love you!",
       "I feel so sad today.",
       "This is the best day ever!",
       "I can't stand the rain.",
       "Everything is going so well.",
       "I hate waiting in line.",
       "The weather is nice, but it's cold.",
       "I'm so proud of my achievements.",
       "I am very upset with the decision.",
       "I am feeling optimistic about the future."
   ]

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处理情绪并准备数据：
对于每个句子，我们对其情感进行分类并提取分数。根据情绪标签（积极或消极），我们为图表中的条形指定颜色。积极的句子将是绿色的，而消极的句子将是红色的。

   scores = []
   colors = []

   for sentence in sentences:
       result = classifier(sentence)
       sentiment = result[0]['label']
       score = result[0]['score']
       scores.append(score)

       if sentiment == "POSITIVE":
           colors.append("green")
       else:
           colors.append("red")

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创建条形图：
我们使用 matplotlib 创建条形图。每个条形的高度代表一个句子的情感分数，颜色区分积极和消极情绪。

   plt.figure(figsize=(10, 6))
   bars = plt.bar(sentences, scores, color=colors)

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添加标签并调整布局：
我们通过旋转 x 轴标签以提高可读性、添加标题以及调整布局以获得最佳间距来自定义绘图的外观。

   plt.xlabel('Sentences')
   plt.ylabel('Sentiment Score')
   plt.title('Sentiment Analysis of 10 Sentences\n')  # Added newline here
   plt.xticks(rotation=45, ha="right")
   plt.subplots_adjust(top=0.85)  # Adjust the top spacing
   plt.tight_layout()  # Adjusts the rest of the layout

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在条形顶部显示情绪分数：
我们还在每个条形的顶部显示情绪得分，以使图表提供更多信息。

pip install transformers torch matplotlib

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显示图：
最后，使用 plt.show() 显示图表，它渲染绘图。

import matplotlib.pyplot as plt
from transformers import pipeline
from transformers import AutoTokenizer, AutoModelForSequenceClassification

# Load pre-trained model and tokenizer
model_name = "distilbert-base-uncased-finetuned-sst-2-english"
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Initialize the sentiment-analysis pipeline
classifier = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)

# List of 10 sentences for sentiment analysis
sentences = [
    "I love you! I love you! I love you!",
    "I feel so sad today.",
    "This is the best day ever!",
    "I can't stand the rain.",
    "Everything is going so well.",
    "I hate waiting in line.",
    "The weather is nice, but it's cold.",
    "I'm so proud of my achievements.",
    "I am very upset with the decision.",
    "I am feeling optimistic about the future."
]

# Prepare data for the chart
scores = []
colors = []

for sentence in sentences:
    result = classifier(sentence)
    sentiment = result[0]['label']
    score = result[0]['score']
    scores.append(score)

    # Color bars based on sentiment: Positive -> green, Negative -> red
    if sentiment == "POSITIVE":
        colors.append("green")
    else:
        colors.append("red")

# Create a bar chart
plt.figure(figsize=(10, 6))
bars = plt.bar(sentences, scores, color=colors)

# Add labels and title with a line break
plt.xlabel('Sentences')
plt.ylabel('Sentiment Score')
plt.title('Sentiment Analysis of 10 Sentences\n')  # Added newline here
plt.xticks(rotation=45, ha="right")

# Adjust spacing with top margin (to add ceiling space)
plt.subplots_adjust(top=0.85)  # Adjust the top spacing (20px roughly equivalent to 0.1 top margin)

plt.tight_layout()  # Adjusts the rest of the layout

# Display the sentiment score on top of the bars
for bar in bars:
    yval = bar.get_height()
    plt.text(bar.get_x() + bar.get_width() / 2, yval + 0.02, f'{yval:.2f}', ha='center', va='bottom', fontsize=9)

# Show the plot
plt.show()

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