[Python NLTK] Practical case: Sentiment analysis, insight into user emotions

Sentiment analysis, also known as opinion mining, is an important branch of natural language processing, aiming to understand and identify emotions and sentiments in text. Sentiment analysis is widely used in many fields, such as public opinion analysis, customer satisfaction analysis, product evaluation analysis, etc.

In this tutorial, we will use the python NLTK library to implement sentiment analysis and demonstrate how to gain insight into user emotions. First, we need to import the necessary libraries:

import nltk
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

Next, we need to download and load the emotion dictionary. NLTK provides many sentiment dictionaries, one of which is VADER (Valence Aware Dictionary and sEntiment Reasoner). We can use the following code to download and load the VADER dictionary:

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

After loading the dictionary, we can perform sentiment analysis on the text. NLTK provides many sentiment analysis functions, one of the commonly used functions is SentimentIntensityAnalyzer.polarity_scores(). We can use this function to calculate the sentiment polarity of the text. The polarity range is [-1, 1], where -1 represents negative sentiment, 0 represents neutral sentiment, and 1 represents positive sentiment.

text = "这部电影真是一部杰作！"
score = analyzer.polarity_scores(text)
print(score)

The output result is:

scores = [analyzer.polarity_scores(text) for text in texts]
polarity = [score["compound"] for score in scores]
plt.hist(polarity, bins=10)
plt.xlabel("情感极性")
plt.ylabel("文本数量")
plt.title("情感分析结果")
plt.show()

By plotting the results of sentiment analysis, we can visually see the sentiment distribution of the text and extract valuable information from it.

Hope this article is helpful to you. If you have any questions or suggestions, please feel free to contact me.

The above is the detailed content of [Python NLTK] Practical case: Sentiment analysis, insight into user emotions. For more information, please follow other related articles on the PHP Chinese website!