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).
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.)
7. Monitor your model’s performance
Finally, don’t forget to monitor your model’s performance on real data!
Conclusion
The design of sentiment analysis processing systems varies depending on the needs and capabilities of a given company. In this article, we described the simplest, most classic use case.
FAQs
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What is sentiment analysis?
Answer: Sentiment analysis is a type of natural language processing (NLP) technique used to determine the emotional tone or attitude conveyed by a piece of text, such as a customer review or a social media post. -
Why is sentiment analysis important?
Answer: Sentiment analysis is important because it can help businesses understand customer opinions and opinions, which can inform product development, marketing strategies, and customer service improvements. - How do I get started with sentiment analysis?
Answer: You can start by following the 7 key steps outlined in this article, including choosing your content, gathering your dataset, splitting your dataset, training a machine learning model, validating your model, deploying your model, and monitoring your model’s performance.
