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Clothing Reviews Sentiment Prediction 

Clothing Reviews Sentiment Prediction 


Sentiment analysis is a type of Natural Language Processing(NLP) that analyzes the sentiments from a text. Through NLP, reviews are classified to positive and negative sentiments. In this project, we will analyze clothing reviews using Click AI.

 - Sentiment analysis using clothing review data

Create Model

1. Check Data

[Womens Clothing E-Commerce Reviews.csv]
 - Reviews will not be preprocessed for training.
 - 'Review scores' will range from 1(negative) to 5(positive).

2. Build Model |  Create a review score prediction model using CLICK AI's natural language process model

2.1 Model Development

[Main Page → Click on 'Create a new project.']

['Adding data' → Upload data file to Click AI.]

[Select uploaded data file from the dataset list → Click on 'Start AI.']

2.2 Model Configuration

[Select your preferred learning type, preferred method, and the value you would like to analyze/predict.]
 - The training data is composed of natural language. Select 'Natural language' as your learning type.
 - To develop a model focused on maximizing its accuracy, select 'with higher accuracy' as your preferred way.
 - Select 'Womens Clothing E-Commerce Reviews.csv' as your value you want to predict.
 - Click on (?) for more details about each options.

2.3 Data Preprocessing

['Summary' → Decide which columns from the data set you would like to use to train the model.]
 - The columns will be automatically unchosen from being used as part of the training data.

[Data Preprocessing → Decide which columns you would like to preprocess.]
 - If you would like to preprocess data, continue by clicking on 'Preprocessing.'

[Choose preferred preprocessing methods and click 'confirm.']

['Data' → View your data file.]

2.4 Model Generation

[After all of the above steps are completed, press the 'Start' button to begin processing the AI model.]

[All of the processed models will appear in the list at the bottom of the page.]

3. View Details | Identify important factors for your model
 - Let's find out the factors that have the biggest impact on the predictions.

[API Connection→ You can connect model results through API connections.]
  - API sources are available for multiple programming languages.

4. Predict Data | Make single or multiple predictions using your model

4.1 Single Prediction
 - Predict review scores for individuals cases by entering individual reviews.

[Training Model 1→ Click on 'Single prediction']
 - Make single predictions using the [Training Model 1]
 - Manually enter texts to make predictions.

[The predicted result is displayed on the right.]
 - 'It is good' is classified as a positive review with a score of 4.

5.2 Collective Prediction 
- Enter data to the downloaded collective prediction template, and run predictions for multiple reviews.

[Training Model 1→ Click on 'Collective prediction']
 - Make collective predictions using the [Training Model 11].

[Click on 'Download template for prediction' to download the prediction template.]

[Enter values to the prediction template (left), and prepare a prediction data set (right) to feed into the prediction model.]

[Upload the prepared prediction data, and proceed by pressing on 'Next'.]

[Prediction results will be available on a new csv file. The last column displays the prediction result of each review.]
 - Collective prediction may require some time. You can download the prediction results from the notifications tab after completion.

5.3 Auto-Labeling
 - Enter data to the downloaded auto-labeling prediction template, and run predictions for multiple reviews.

[Training Model 1 → Click on 'Auto-labeling]
 - Make auto-labeling prediction using the [Training Model 1].

[Click on 'Download template for prediction' to download the prediction template.]

[Enter values to the prediction template (left), and prepare a prediction data set (right) to feed into the prediction model.]

[Upload the prepared prediction data, and proceed by pressing on 'Next'.]

[Prediction results will be available on a new csv file. The last column displays the prediction result of each customer review.]
 - Auto-labeling prediction may require some time to complete. You can download the prediction results from the notifications tab after completion.

6. Share | Share your AI model with API

[Training Model 1 → Click on 'Details']

[Sharing your service app]
 - The URL link will direct you to shared page of the model.
 - Share your model using the URL link.
 - Others may have access to your model through the URL link. If somebody uses the model to make predictions, it will be counted from your prediction count.
 - 'Analyze' feature offers both explainable AI and visualizations on the shared link.
 - You can put your AI model on sale by using the AI store. For details, please check AI 스토어.

Consult with an expert now.

Consult with an expert now.