Senta is an open-source sentiment analysis system by Baidu’s technical team. Sentiment analysis aims to automatically identify and extract subjective information such as tendencies, positions, evaluations and opinions from texts. It includes a variety of tasks, such as sentence level emotion classification, evaluation object level emotion classification, opinion extraction, emotion classification and so on. Sentiment analysis is an important research direction of artificial intelligence and has high academic value. At the same time, sentiment analysis has important applications in consumer decision-making, public opinion analysis, personalized recommendation and other fields.

In order to facilitate R&D personnel and business partners to share the leading emotion analysis technology, Baidu has open source the SKEP-based emotion pre-training code and the Chinese-English emotion pre-training model in Senta. Users only need a few lines of code to realize the SkeP-based emotion pre-training and model prediction function.

Usage

1 Environmental preparation

PaddlePaddle installation

This project depends on PaddlePaddle 1.6.3. After PaddlePaddle is installed, dynamic library paths such as CUDA, cuDNN, NCCL2 need to be added to environment variable LD_LIBRARY_PATH in time. Otherwise, related library errors will be reported during training.

Detailed installation documentation:
https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation/docs/zh/install/pip/linux-pip.html

pip installation is recommended:

python -m pip install paddlepaddle-gpu==1.6.3.post107-i https://mirror.baidu.com/pypi/simple

senta project python package dependencies

Support for Python 3 version 3.7; The other python package dependencies in the project are listed in the requirements.txt file in the root directory and are installed with the following command:

python -m pip install -r requirements.txt

Environment variable added

Modify the environment variables in./env.sh, including the python, CUDA, cuDNN, NCCL2, PaddlePaddle related environment variables.

PaddlePaddle Environment variable description:
https://www.paddlepaddle.org.cn/documentation/docs/zh/1.6/flags_cn.html < / p >

2 Installation project

Senta warehouse supports both pip installation and source installation. PaddlePaddle needs to be installed before using pip or source installation.

pip installation

python -m pip install Senta

Source code installation

git clone  https://github.com/baidu/Senta.git
cd Senta
python -m pip install .

3 Usage Method

from senta import Senta

my_senta = Senta()

# Get the currently supported emotion pre-training model, We open SKEP models initialized with ERNIE 1.0 large(Chinese), ERNIE 2.0 large(English), and RoBERTa large(English) 
print(my_senta.get_support_model()) # ["ernie_1.0_skep_large_ch", "ernie_2.0_skep_large_en", "roberta_skep_large_en"]

# Get currently supported prediction tasks 
print(my_senta.get_support_task()) # ["sentiment_classify", "aspect_sentiment_classify",  "extraction"]

# Select whether to use gpu
use_cuda = True # Set True or False

# Predicting Chinese sentence-level emotion classification task 
my_senta.init_model(model_class="ernie_1.0_skep_large_ch", task="sentiment_classify", use_cuda=use_cuda)
texts = [" Sun Yat-sen University is the first institution of Learning in Lingnan "]
result = my_senta.predict(texts)
print(result)
# Predicting emotion classification tasks at the Chinese evaluation object level 
my_senta.init_model(model_class="ernie_1.0_skep_large_ch", task="aspect_sentiment_classify", use_cuda=use_cuda)
texts = [" Baidu is a high-tech company "]
aspects = [" Baidu "]
result = my_senta.predict(texts, aspects)
print(result)

# Predict Chinese opinion extraction task 
my_senta.init_model(model_class="ernie_1.0_skep_large_ch", task="extraction", use_cuda=use_cuda)
texts = [" Tang Jia Sanshao, real name Zhang Wei. "]
result = my_senta.predict(texts, aspects)
print(result)

# Predicting English sentence-level sentiment classification Tasks (based on SKEP-ERNIE2.0 model) 
my_senta.init_model(model_class="ernie_2.0_skep_large_en", task="sentiment_classify", use_cuda=use_cuda)
texts = ["a sometimes tedious film ."]
result = my_senta.predict(texts)
print(result)

# Predicting emotion classification tasks at the English Evaluation object level (based on SKEP-ERNIE2.0 model) 
my_senta.init_model(model_class="ernie_2.0_skep_large_en", task="aspect_sentiment_classify", use_cuda=use_cuda)
texts = ["I love the operating system and the preloaded software."]
aspects = ["operating system"]
result = my_senta.predict(texts, aspects)
print(result)

# Predicting English Opinion Extraction Tasks (based on SKEP-ERNIE2.0 model) 
my_senta.init_model(model_class="ernie_2.0_skep_large_en", task="extraction", use_cuda=use_cuda)
texts = ["The JCC would be very pleased to welcome your organization as a corporate sponsor  ."]
result = my_senta.predict(texts)
print(result)

# Predicting English sentence-level sentiment classification tasks (based on SKEP-RoBERTa model) 
my_senta.init_model(model_class="roberta_skep_large_en",  task="sentiment_classify", use_cuda=use_cuda)
texts = ["a sometimes tedious film ."]
result = my_senta.predict(texts)
print(result)

# Predicting emotion classification tasks at the English evaluation object level (based on SKEP-RoBERTa model) 
my_senta.init_model(model_class="roberta_skep_large_en",  task="aspect_sentiment_classify", use_cuda=use_cuda)
texts = ["I love the operating system and the preloaded software."]
aspects = ["operating system"]
result = my_senta.predict(texts, aspects)
print(result)

# Predicting English opinion Extraction Tasks (based on SKEP-RoBERTa model) 
my_senta.init_model(model_class="roberta_skep_large_en",  task="extraction", use_cuda=use_cuda)
texts = ["The JCC would be very pleased to welcome your organization as a corporate sponsor  ."]
result = my_senta.predict(texts)
print(result)

Specific effect

SKEP uses affective knowledge to enhance the pre-training model, and comprehensively outperforms SOTA in 14 typical tasks of Chinese-English affective analysis, with an average improvement of about 2% compared with the original SOTA. The specific results are shown in the table below:

Sentiment analysis system based on SKEP algorithm插图1

You can read more on your own.

资源下载此资源为免费资源立即下载

Telegram:@John_Software

collect(0) Like (0)

Disclaimer: This article is published by a third party and represents the views of the author only and has nothing to do with this website. This site does not make any guarantee or commitment to the authenticity, completeness and timeliness of this article and all or part of its content, please readers for reference only, and please verify the relevant content. The publication or republication of articles by this website for the purpose of conveying more information does not mean that it endorses its views or confirms its description, nor does it mean that this website is responsible for its authenticity.

Ictcoder Free Source Code Sentiment analysis system based on SKEP algorithm https://ictcoder.com/sentiment-analysis-system-based-on-skep-algorithm/

lllll

Share free open-source source code

Previous article： Mockcat, a front - and back-end separate development tool set

Next article： Linux Foundation Open Source: real-time 3D engine for multiple platforms

Q&A

Ordering Process

1. Automatic: After making an online payment, click the (Download) link to download the source code; 2. Manual: Contact the seller or the official to check if the template is consistent. Then, place an order and make payment online. The seller ships the goods, and both parties inspect and confirm that there are no issues. ICTcoder will then settle the payment for the seller. Note: Please ensure to place your order and make payment through ICTcoder. If you do not place your order and make payment through ICTcoder, and the seller sends fake source code or encounters any issues, ICTcoder will not assist in resolving them, nor can we guarantee your funds!

View details

Transactions and Disputes

1. Default transaction cycle for source code: The seller manually ships the goods within 1-3 days. The amount paid by the user will be held in escrow by ICTcoder until 7 days after the transaction is completed and both parties confirm that there are no issues. ICTcoder will then settle with the seller. In case of any disputes, ICTcoder will have staff to assist in handling until the dispute is resolved or a refund is made! If the buyer places an order and makes payment not through ICTcoder, any issues and disputes have nothing to do with ICTcoder, and ICTcoder will not be responsible for any liabilities!

View details

Matters needing attention

1. ICTcoder will permanently archive the transaction process between both parties and snapshots of the traded goods to ensure the authenticity, validity, and security of the transaction! 2. ICTcoder cannot guarantee services such as "permanent package updates" and "permanent technical support" after the merchant's commitment. Buyers are advised to identify these services on their own. If necessary, they can contact ICTcoder for assistance; 3. When both website demonstration and image demonstration exist in the source code, and the text descriptions of the website and images are inconsistent, the text description of the image shall prevail as the basis for dispute resolution (excluding special statements or agreements); 4. If there is no statement such as "no legal basis for refund" or similar content, any indication on the product that "once sold, no refunds will be supported" or other similar declarations shall be deemed invalid; 5. Before the buyer places an order and makes payment, the transaction details agreed upon by both parties via WhatsApp or email can also serve as the basis for dispute resolution (in case of any inconsistency between the agreement and the description of the conflict, the agreement shall prevail); 6. Since chat records and email records can serve as the basis for dispute resolution, both parties should only communicate with each other through the contact information left on the system when contacting each other, in order to prevent the other party from denying their own commitments. 7. Although the probability of disputes is low, it is essential to retain important information such as chat records, text messages, and email records, in case a dispute arises, so that ICTcoder can intervene quickly.

View details

ICTcoder reminds and declares

1. As a third-party intermediary platform, ICTcoder solely protects transaction security and the rights and interests of both buyers and sellers based on the transaction contract (product description, agreed content before the transaction); 2. For online trading projects not on the ICTcoder platform, any consequences are unrelated to this platform; regardless of the reason why the seller requests an offline transaction, please contact the administrator to report.

View details