Purchase decision analysis of product reviews for E-commerce

Service activation and resource plan purchase

Before you start, ensure that the service is activated. After activation, you can purchase a resource plan.

• Activate service: Activation link

• Purchase resource plan: Purchase link

Service invocation and testing

For information about model invocation, see Model invocation.

For SDK examples, see SDK examples.

Debug

Configure access credentials using environment variables

1. Notes:

   1. An AccessKey pair of an Alibaba Cloud account has all API access permissions. This poses a high security risk. We recommend that you create and use a Resource Access Management (RAM) user to make API calls or perform routine O&M. To create a RAM user, log on to the RAM console.

   2. To avoid security risks, do not hard-code your AccessKey ID and AccessKey secret in your code. Instead, configure them as environment variables.

2. Configuration for Linux and macOS

   export NLP_AK_ENV=<access_key_id>
   export NLP_SK_ENV=<access_key_secret>

   Replace <access_key_id> with your AccessKey ID and <access_key_secret> with your AccessKey secret. For more information about how to obtain an AccessKey ID and an AccessKey secret, see Step 2: Obtain an AccessKey pair.

3. Method for configuring Windows systems

   1. Create the NLP_AK_ENV and NLP_SK_ENV environment variables, and set them to your AccessKey ID and AccessKey secret.

   2. Restart Windows.

Java code example

/**
 * An AccessKey pair of an Alibaba Cloud account has all API access permissions. This poses a high security risk. Create and use a RAM user to make API calls or perform routine O&M. To create a RAM user, log on to the RAM console.
 * This example shows how to store the AccessKey ID and AccessKey secret in environment variables. You can also store them in a configuration file as needed.
 * To avoid security risks, do not hard-code your AccessKey ID and AccessKey secret in your code.
 */
String accessKeyId = System.getenv("NLP_AK_ENV");
String accessKeySecret = System.getenv("NLP_SK_ENV");
DefaultProfile defaultProfile = DefaultProfile.getProfile("cn-hangzhou",accessKeyId,accessKeySecret);
IAcsClient client = new DefaultAcsClient(defaultProfile);

RunPreTrainServiceRequest request = new RunPreTrainServiceRequest();
request.setServiceName("NLP_ner_pretrained_review_multidim_ecom");
request.setPredictContent("Received the children's masks. Fast logistics, good service. A must-have for school during the pandemic. The price is better than in physical stores. I have purchased them many times!");
RunPreTrainServiceResponse response = client.getAcsResponse(request);
System.out.println(response.getPredictResult());

Python code example

# Install dependencies
pip install aliyun-python-sdk-core
pip install aliyun-python-sdk-nlp-automl

# -*- coding: utf8 -*-
import json
import os

from aliyunsdkcore.client import AcsClient
from aliyunsdkcore.acs_exception.exceptions import ClientException
from aliyunsdkcore.acs_exception.exceptions import ServerException
from aliyunsdknlp_automl.request.v20191111 import RunPreTrainServiceRequest

# An AccessKey pair of an Alibaba Cloud account has all API access permissions. This poses a high security risk. Create and use a RAM user to make API calls or perform routine O&M. To create a RAM user, log on to the RAM console.
# This example shows how to store the AccessKey ID and AccessKey secret in environment variables. You can also store them in a configuration file as needed.
# To avoid security risks, do not hard-code your AccessKey ID and AccessKey secret in your code.
access_key_id = os.environ['NLP_AK_ENV']
access_key_secret = os.environ['NLP_SK_ENV']

# Initialize AcsClient instance
client = AcsClient(
  access_key_id,
  access_key_secret,
  "cn-hangzhou"
);

content = 'Received the children\'s masks. Fast logistics, good service. A must-have for school during the pandemic. The price is better than in physical stores. I have purchased them many times!'

# Initialize a request and set parameters
request = RunPreTrainServiceRequest.RunPreTrainServiceRequest()
request.set_ServiceName('NLP_ner_pretrained_review_multidim_ecom')
request.set_PredictContent(content)

# Print response
response = client.do_action_with_exception(request)
resp_obj = json.loads(response)
predict_result = json.loads(resp_obj['PredictResult'])
print(json.dumps(predict_result['data'],ensure_ascii=False))

Request parameters

Example of PredictResult content

{
  "records": {
    "comparison": [
      {
        "prob": 0.9929813610182868,
        "offset": [26, 35],
        "span": "The price is better than in physical stores"
      }
    ],
    "purchase_motivation^scenario": [
      {
        "prob": 0.97239750623703,
        "offset": [21, 25],
        "span": "A must-have for school"
      }
    ],
    "user_object": [
      {
        "prob": 0.9865950644016266,
        "offset": [0, 2],
        "span": "children"
      }
    ],
    "usage_scenario": [
      {
        "prob": 0.975536584854126,
        "offset": [21, 25],
        "span": "A must-have for school"
      }
    ],
    "will_repurchase": [
      {
        "prob": 0.9607752660910288,
        "offset": [36, 42],
        "span": "I have purchased them many times"
      }
    ]
  },
  "content": "Received the children's masks. Fast logistics, good service. A must-have for school during the pandemic. The price is better than in physical stores. I have purchased them many times!"
}

Response parameters

List of supported purchase decision labels