Hello everyone, I am Weixue AI. Today I will introduce to you Natural Language Processing Practical Project 17-Research and Application of Fraud Call Recognition Method Based on NLP Model. I believe that all of you have watched the realistic fraud movie "All or Nothing" recently. Well, the movie mainly revolves around cross-border online fraud, and the movie is based on tens of thousands of real fraud cases. With the rapid development of technology, fraudulent calls have become a common means of crime, posing a huge threat to people's lives and financial security. There are various forms of fraudulent calls, such as fake bank staff, credit information, football lottery inside information, false claims of winning prizes, false investment opportunities, etc., which have brought great distress and loss to people.
Contents
1. Introduction
A. Research background and motivation
2. Overview of scam call identification methods
A. Definition and classification of scam calls
B. Review of traditional identification methods
C. Application potential of NLP in scam call identification
3. Data collection and preprocessing
A. Data source and description
B. Data preprocessing technology
1. Telephone call recording conversion and segmentation
2. Text conversion and cleaning
3. Feature extraction and selection
4. Application of NLP technology in scam call identification
A. Text feature extraction and representation
1. Text vectorization method
2. Keyword extraction and frequency statistics
3. Semantic representation model (such as Word2Vec, BERT, etc.)
B. Model training and evaluation
1. Supervised learning methods (such as SVM, decision tree, etc.)
2. Deep learning methods (such as RNN, CNN, etc.)
C. Model performance evaluation indicators
1. Accuracy rate, recall rate and F1 value
2. ROC curve and AUC value
V. Scam call identification code sample
A. Data sample loading
B. Model training
1. TF-IDF model building and training
2. LSTM model building and training
6. Conclusions and prospects
A. Summary of main research work
B. Significance and limitations of research results
C. Follow-up research direction and room for expansion
I. Introduction
A. Research background and motivation
In recent months, large-scale fraud activities have occurred in northern Myanmar, organized and carried out by some criminal gangs using overseas resources and advantages. These fraud gangs use a variety of methods and forms, including telephone fraud, Internet fraud, and posing as official institutions. They usually use technical means to hide their real identities and phone numbers, making it difficult for victims to tell the real from the fake.
The reason why these fraudulent gangs are rampant is that, on the one hand, there is border contact in northern Myanmar, which makes it difficult for the police to hunt down; Manhunt.
In the face of such fraudulent gangs, we need to strengthen international cooperation and information sharing in order to obtain relevant intelligence in a timely manner and take effective crackdown measures. At the same time, the public should also increase their awareness of fraud risks, remain vigilant, not easily trust calls or messages from strangers, and take preventive measures, such as refusing to provide sensitive personal information, verifying the authenticity of identities, and reporting crimes in a timely manner. Only through multi-party cooperation and collective wisdom can we better curb the activities of fraudulent gangs and protect people's financial security.
The purpose of this study is to provide a method for identifying fraudulent calls based on natural language processing (NLP), so as to effectively solve the threat of fraudulent calls to people. The specific objectives include: firstly, define and classify fraudulent calls, and clarify the research objects; secondly, review the traditional recognition methods, analyze their advantages, disadvantages and limitations; finally, discuss the application potential of NLP technology in fraudulent call recognition, and provide a basis for constructing A more accurate recognition model provides a reference.
2. Overview of fraud phone identification methods
A. Definition and classification of scam calls
Fraudulent calls refer to telephone communication activities that use mobile phones or landlines to carry out fraudulent activities. According to the different methods and purposes of fraud, fraudulent calls can be divided into multiple categories, such as bank fraud, lottery fraud, loan fraud, credit fraud, express compensation fraud, AI fraud, etc. Each type of scam calls has its own unique characteristics and purpose, so it is necessary to adopt corresponding identification methods for different types of scam calls.
B. Review of Traditional Identification Methods
In the past, fraudulent call identification methods mainly relied on the blacklist of phone numbers, the matching of specific keywords, and the formulation of manual rules. However, these methods have some limitations, such as high misjudgment rate and unstable recognition effect. Therefore, it is of great significance to develop an NLP-based method for identifying fraudulent calls.
C. The application potential of NLP in the identification of fraudulent calls
NLP technology has broad application potential in fraud phone identification. First of all, NLP can understand the content of the call and the intention of the speaker through semantic analysis, sentiment analysis and other technologies, so as to more accurately judge whether the call is a fraud call. Secondly, NLP can also build a fraud call recognition model by mining a large amount of text data, so that it has better generalization ability and adaptability.
This article will discuss in detail the application potential of NLP technology in the identification of fraudulent calls, and propose an NLP-based recognition model construction method, aiming to improve the accuracy and stability of recognition, so as to effectively prevent the occurrence of fraudulent calls. The results of this study are of great significance to protect people's property safety and maintain social stability.
3. Data collection and preprocessing
A. Data sources and descriptions
In fraudulent call identification, data sources can include telephone call recordings and text records. Recordings of phone calls are collected by phone recording devices or software and contain recordings of calls from different phone numbers. Transcripts are textual information generated during a telephone call, such as a transcript from a call center or a user-provided transcription.
B. Data Preprocessing Techniques
Data preprocessing is the process of cleaning and transforming raw data before further analysis. In fraud call identification, commonly used data preprocessing techniques include telephone call recording conversion and segmentation, text conversion and cleaning, and feature extraction and selection.
1. Conversion and segmentation of telephone call recordings
Telephone call recordings need to be converted and segmented to extract useful information. Conversion involves converting call recordings from an audio format to a processable digital representation, such as a wave form or a spectrogram. Segmentation is to divide the entire call recording into smaller segments for subsequent analysis.
2. Text conversion and cleaning
For a text record, it first needs to be converted into a machine-readable form, such as converting the text to a string or a sequence of tokens. Then, clean the text, remove useless characters, punctuation marks and stop words, and unify capitalization and other operations to reduce the impact of noise on subsequent analysis.
3. Feature extraction and selection
Feature extraction is the process of extracting useful information from raw data in order to train a model for classification or recognition. In fraud call recognition, speech features (such as spectrogram, fundamental frequency, etc.) and text features (such as keywords, part of speech, syntactic structure, etc.) can be extracted. Feature selection is to select the most relevant and discriminative features from many features to reduce model complexity and improve classification performance.
4. The application of NLP technology in the identification of fraudulent calls
A. Text Feature Extraction and Representation
Extraction and representation of text features are very important steps in fraud call recognition, they are used to convert raw text data into machine understandable form.
1. Text vectorization method
Text vectorization is one of the methods to convert text into vector representation. Commonly used text vectorization methods include Bag of Words and TF-IDF. The bag-of-words model represents text as frequency vectors of words in a vocabulary, ignoring the order and grammatical structure of words. TF-IDF considers the importance of words in the text, and obtains vector representations by calculating word frequency and inverse document frequency.
2. Keyword extraction and frequency statistics
Keyword extraction is to extract significant words or phrases from the text. Commonly used keyword extraction algorithms include word frequency-based, TF-IDF, TextRank, etc. Keyword extraction can help identify common fraudulent means or key information in scam calls.
3. Semantic representation model
The semantic representation model converts text into a vector representation in semantic space by learning the semantic relationship between words. Word2Vec is a neural network-based semantic representation model that maps words to a continuous vector space. BERT is a pre-trained language model that understands the contextual relationship between words and produces more accurate text representations.
B. Model training and evaluation
In fraudulent call identification, the training and evaluation of the model is to build a system that can automatically determine whether a call is fraudulent or not.
1. Supervised learning method
Supervised learning is a method of training a model through labeled training data. In scam call identification, machine learning algorithms such as support vector machine (SVM) and decision tree can be used for classification. These algorithms learn from samples with known labels and build a model capable of classifying new samples.
2. Deep learning methods
Deep learning methods train and classify by constructing a multi-layer neural network model. In scam call identification, deep learning models such as recurrent neural network (RNN) and convolutional neural network (CNN) can be used. These models are able to learn complex features in phone call recordings or text data to improve classification accuracy.
C. Model Performance Evaluation Metrics
In order to evaluate the performance of the model, it is necessary to use some indicators to measure the accuracy and stability of its classification results.
1. Accuracy, recall and F1 value
Accuracy measures the ability of the model to correctly classify samples, and recall measures the ability of the model to find all positive samples. The F1 value is a comprehensive evaluation index of precision and recall, which is used to balance the relationship between precision and recall.
2. ROC curve and AUC value
The ROC curve is a curve with the false positive rate on the horizontal axis and the true positive rate on the vertical axis. The AUC value represents the area under the ROC curve, which is used to measure the overall performance of the model classification performance. The larger the AUC value, the better the classification effect of the model.
5. Scam identification code sample
A. Data Sample Loading
Suppose our sample dataset is a CSV file with two columns: "text" and "label". Among them, the "text" column contains the content of the phone call recording or text record, and the "label" column is used to indicate whether the text is a fraudulent call, and the value of the label is 0 (non-fraud) or 1 (fraud).
文本,标签
"您好，这里是ABC银行，我们怀疑您的银行账户出现异常活动，请提供您的个人信息以验证身份。",1
"尊敬的客户，您已被选中参加我们的奖品抽奖活动，只需支付一小笔费用即可获得高额奖金。",1
"您好，我是申通快递，您买的一个包裹，公司给您弄丢了，这里需要加我们的理赔客服对您快递进行理赔200元。",1
"您好，这是一条关于您的快递的通知，由于地址错误，需要支付额外的费用进行重新寄送。",0
"您好，我是您的移动运营商客服，您的账户余额已不足，请及时充值以避免影响正常使用。",0
"尊敬的客户，您的手机尾号2345的机主，目前已经欠费10元，将会影响您的宽带使用。",0
The steps of loading data can be implemented using Python's pandas library:
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.svm import SVC
# 加载CSV文件
data = pd.read_csv("data.csv")
# 查看数据集信息
print(data.info())
# 划分特征和标签
X = data["文本"]
y = data["标签"]
B. Model Training
1. TF-IDF model training
Next, NLP technology can be used for text feature extraction and representation, and a model can be established to identify fraudulent text. Common methods include using bag-of-words models, TF-IDF, or deep learning models (such as RNN, CNN).
# 分割训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# 特征提取和表示（使用TF-IDF）
vectorizer = TfidfVectorizer()
X_train_tfidf = vectorizer.fit_transform(X_train)
X_test_tfidf = vectorizer.transform(X_test)
# 创建分类模型（支持向量机）
svm_model = SVC()
# 模型训练
svm_model.fit(X_train_tfidf, y_train)
# 模型评估
accuracy = svm_model.score(X_test_tfidf, y_test)
print("模型准确率：", accuracy)
Here, TF-IDF is used for feature extraction and representation of the text, and the text is converted into a vector form. Next, create and train a support vector machine classification model. Finally, evaluate the performance of the model by making predictions on the test set and calculating the accuracy.
2. LSTM model training
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import accuracy_score
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
from torch.nn.utils.rnn import pad_sequence
# 自定义数据集类
class TextDataset(Dataset):
def __init__(self, X, y):
self.X = X
self.y = y
def __len__(self):
return len(self.X)
def __getitem__(self, index):
return self.X[index], self.y[index]
# 自定义LSTM模型
class LSTMModel(nn.Module):
def __init__(self, vocab_size, embedding_dim, hidden_dim, output_dim):
super(LSTMModel, self).__init__()
self.embedding = nn.Embedding(vocab_size, embedding_dim)
self.lstm = nn.LSTM(embedding_dim, hidden_dim, batch_first=True)
self.fc = nn.Linear(hidden_dim, output_dim)
def forward(self, x):
embedded = self.embedding(x)
output, _ = self.lstm(embedded)
output = self.fc(output[:, -1, :])
return output.squeeze()
# 加载CSV文件
data = pd.read_csv("data.csv")
# 划分特征和标签
X = data["文本"]
y = data["标签"]
# 文本预处理
tokenizer = Tokenizer()
tokenizer.fit_on_texts(X)
word_index = tokenizer.word_index
sequences = tokenizer.texts_to_sequences(X)
X = pad_sequences(sequences)
# 标签编码
label_encoder = LabelEncoder()
y = label_encoder.fit_transform(y)
# 分割训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# 创建数据加载器
train_dataset = TextDataset(torch.tensor(X_train), torch.tensor(y_train))
test_dataset = TextDataset(torch.tensor(X_test), torch.tensor(y_test))
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)
# 定义模型超参数
vocab_size = len(word_index) + 1
embedding_dim = 100
hidden_dim = 64
output_dim = 1
# 创建模型实例和优化器
model = LSTMModel(vocab_size, embedding_dim, hidden_dim, output_dim)
optimizer = optim.Adam(model.parameters(), lr=0.001)
criterion = nn.BCEWithLogitsLoss()
# 模型训练
def train(model, dataloader, optimizer, criterion):
model.train()
running_loss = 0.0
for inputs, labels in dataloader:
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels.float().unsqueeze(1))
loss.backward()
optimizer.step()
running_loss += loss.item() * inputs.size(0)
epoch_loss = running_loss / len(dataloader.dataset)
return epoch_loss
# 模型评估
def evaluate(model, dataloader):
model.eval()
predictions = []
true_labels = []
with torch.no_grad():
for inputs, labels in dataloader:
outputs = model(inputs)
preds = torch.round(torch.sigmoid(outputs))
predictions.extend(preds.tolist())
true_labels.extend(labels.tolist())
accuracy = accuracy_score(true_labels, predictions)
return accuracy
num_epochs = 10
for epoch in range(num_epochs):
train_loss = train(model, train_loader, optimizer, criterion)
test_acc = evaluate(model, test_loader)
print(f"Epoch [{
epoch+1}/{
num_epochs}], Train Loss: {
train_loss:.4f}, Test Accuracy: {
test_acc:.4f}")
In the above code, I first defined two custom classes: TextDataset is used to create a custom dataset, and LSTMModel is a simple LSTM model.
Through training, we can identify whether the text is fraudulent information.
6. Summary of main research work
A. Summary of main research work
By designing and implementing a fraud call identification system, and verifying the application scenarios and effects, the summary is as follows:
A fraud call identification system based on artificial intelligence technology is proposed, which can effectively identify and block threats from fraud calls. In the application scenarios and effect verification of the system, a high-accuracy recognition result has been achieved, and it has good real-time performance.
Continuously improve and optimize the system through user feedback and improvement suggestions to enhance user experience and security.
B. Significance and limitations of the findings
Our research results have important significance and practical application value:
1. Help users effectively identify and block fraudulent calls, and protect user call security.
2. Improve the trust and reliability of calls and promote the development of the communication industry.
However, our research also has certain limitations:
1. There may be a certain delay in the identification of new types of fraudulent calls, and the model needs to be updated in time to adapt to the new situation.
2. For some phones with poor voice quality, the recognition accuracy may drop.
3. The applicability and scalability of the system need to be further verified in a wider range of scenarios.
C. Follow-up research direction and expansion space
Based on the above work and results, we propose the following follow-up research directions and expansion space:
1. Introduce more deep learning technologies, such as natural language processing and speech emotion analysis, to improve the accuracy and robustness of the system.
2. Carry out data collection and processing of more samples, improve the training set of the system, and improve the system's ability to identify various types of fraudulent calls.
3. Explore cooperation with communication operators, apply fraudulent call identification technology to the network level, and further improve the overall identification effect and coverage.