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How Do You Create A Sentiment Analysis Process?


Are you planning to build a sentiment analysis but don’t know how to start? In this article, you will find 7 key steps that need to perform.

1. Choose your content

First, you have to decide what kind of content you want to analyze. People express emotions differently in a movie review than in email correspondence, and the context influences the process design.

2. Gather your dataset

You need to gather as many labelled data points relevant to your particular type of document as possible. The dataset must contain the document content and a human-assigned label (`positive`, `neutral`, or `negative`).

3. Split your dataset

Now you split your dataset into a training set and a hold-out set. A popular strategy is a random split, with about 20% of samples in the hold-out set.

4. Train a machine learning model

Here’s where you use your training dataset to train a machine learning model to classify your content as positive, neutral or negative (supervised learning, binary classification model). 

The model architecture is up to you, but we recommend training a proven, context-aware NLP model (like BERT). We also recommend not training a model from scratch but instead using a transfer learning technique. 

If you can start with a model that already understands text in your selected languages (because it was trained on a vast corpus of human language to develop associations and understanding of words and phrases), all the better.

You can fine-tune such a model for sentiment analysis tasks, which will provide much better results than if you try to train a model from scratch. Not sure where to start? 

Try our course on how to create and train a sentiment analysis model.

5. Validate your model

Now it’s time to validate your trained machine learning model on your hold-out dataset: do this by evaluating the values of chosen model analysis metrics and decide whether the output is good enough for your application.

6. Deploy your model

If you need real-time predictions, deploy the model as an endpoint. We recommend code-less serving platforms like `Tensorflow Serving` (preferably on a cluster of machines in the cloud for scalability, for example, Vertex AI Endpoints.)

You can learn how to do this in our free sentiment analysis course. And you can integrate external applications with the model over the endpoint’s HTTP API. If you don’t need live predictions, you can just use your trained model in batch prediction mode. 

Here, you can leverage Vertex AI Batch Predictions for the job. And once the process ends, you can import the batch prediction results into your other applications.

7. Monitor your model’s performance

Finally, don’t forget to monitor your model’s performance on real data! 

It might turn out that your actual documents are so different from the training set that the model’s performance isn’t ideal. In such a case, it might help to extend your training set with additional sources of good examples, ultimately re-training the model.