在本文中，我們將使用 Matplotlib 添加情感分析結(jié)果的圖形表示。目標(biāo)是可視化多個句子的情緒分?jǐn)?shù)，并使用條形圖使用不同的顏色區(qū)分積極和消極情緒。

先決條件

確保您安裝了以下庫：

pip install transformers torch matplotlib

• Transformer：用于處理預(yù)先訓(xùn)練的 NLP 模型。
• torch：用于運(yùn)行模型。
• matplotlib：用于創(chuàng)建情感分析結(jié)果的圖形表示。

可視化 Python 代碼

這是更新后的 Python 代碼，它將情感分析與數(shù)據(jù)可視化相集成。

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()

代碼分解

導(dǎo)入必要的庫：
我們導(dǎo)入 matplotlib.pyplot 來創(chuàng)建繪圖和轉(zhuǎn)換器來執(zhí)行情感分析。

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

加載預(yù)訓(xùn)練模型：
我們加載針對 SST-2 數(shù)據(jù)集進(jìn)行情感分析而微調(diào)的 DistilBERT 模型。我們還加載關(guān)聯(lián)的標(biāo)記生成器，將文本轉(zhuǎn)換為模型可讀的標(biāo)記。

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

初始化情感分析管道：
分類器管道是為情感分析而設(shè)置的。該管道負(fù)責(zé)對輸入文本進(jìn)行標(biāo)記、執(zhí)行推理并返回結(jié)果。

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

情感分析句子：
我們創(chuàng)建一個包含 10 個句子的列表來進(jìn)行分析。每句話都是一種獨(dú)特的情感表達(dá)，從非常積極到消極。

   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."
   ]

處理情緒并準(zhǔn)備數(shù)據(jù)：
對于每個句子，我們對其情感進(jìn)行分類并提取分?jǐn)?shù)。根據(jù)情緒標(biāo)簽（積極或消極），我們?yōu)閳D表中的條形指定顏色。積極的句子將是綠色的，而消極的句子將是紅色的。

   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")

創(chuàng)建條形圖：
我們使用 matplotlib 創(chuàng)建條形圖。每個條形的高度代表一個句子的情感分?jǐn)?shù)，顏色區(qū)分積極和消極情緒。

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

添加標(biāo)簽并調(diào)整布局：
我們通過旋轉(zhuǎn) x 軸標(biāo)簽以提高可讀性、添加標(biāo)題以及調(diào)整布局以獲得最佳間距來自定義繪圖的外觀。

   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

在條形頂部顯示情緒分?jǐn)?shù)：
我們還在每個條形的頂部顯示情緒得分，以使圖表提供更多信息。

pip install transformers torch matplotlib

顯示圖：
最后，使用 plt.show() 顯示圖表，它渲染繪圖。

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()

樣本輸出

此代碼的輸出將是一個顯示 10 個句子的情緒分?jǐn)?shù)的條形圖。積極的句子將用綠色條表示，而消極的句子將顯示為紅色條。情緒得分將顯示在每個條形上方，顯示模型的置信水平。

結(jié)論

通過將情感分析與數(shù)據(jù)可視化相結(jié)合，我們可以更好地解讀文本數(shù)據(jù)背后的情感基調(diào)。本文中的圖形表示可以讓您更清楚地了解情緒分布，使您可以輕松發(fā)現(xiàn)文本中的趨勢。您可以將此技術(shù)應(yīng)用于各種用例，例如分析產(chǎn)品評論、社交媒體帖子或客戶反饋。

通過 Hugging Face 的轉(zhuǎn)換器和 matplotlib 的強(qiáng)大組合，可以擴(kuò)展和定制此工作流程以適應(yīng)各種 NLP 任務(wù)。

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