Applied AI & AI in business /

The potential behind sentiment analysis

December 22 2020/3 min read
  • Liliana Lindberg
    Liliana LindbergSolutions Architect

Understanding the customer’s perception of a product, service or brand is essential for any company in any industry. Sentiment analysis extracts the positive or negative score of a written opinion and could more importantly identify why customers feel that way.

02/ What is sentiment analysis?

Sentiment analysis is a common task in Natural Language Processing (NLP) and runs as a type of text classification. An AI model gets trained to identify the emotional tonality of a text as positive, negative or neutral. The granularity of the labels to be predicted can be defined based on the use case needs which can include a wider range from very positive to very negative or finer‐grained emotions such as sadness, happiness or anger. You can choose to frame different problems depending on the data you have available and what is relevant to you.

03/ Why is it important?

For any company, being able to respond quickly to feedback, issues or complaints is critically important to maintain a positive customer experience. AI and specifically sentiment analysis and text classification are powerful tools that in real-time can monitor and identify which aspects from products and offerings are presenting opportunities or threats to customer satisfaction.

Leave the repetitive tasks of analyzing and reviewing countless written end-users interactions to an AI-based system and empower your team to focus on more actionable tasks such as customer engagement; issues mitigation and resolution; and product and service improvements.

04/ How does a solution look on Peltarion?

The Peltarion platform enables you to create tailored models using the latest NLP architectures without writing code. Once a model is created in five easy steps (create, build, train, evaluate and deploy) your application will be able to:

  • Extract sentiment by looking beyond positive and negative words in text. The pretrained BERT model on the platform examines the full semantic context of the words surpassing traditional NLP architectures.
  • Build and train a sentiment analysis model in one language and leverage it across +99 other languages thanks to the multilingual BERT snippet available on the platform. 
  • Achieve high accuracy quickly with a relatively small amount of domain-specific data.
  • Update your models easily and use one-click deployment with minimum investment. The Peltarion platform manages and maintains the infrastructure and frameworks required by your models for testing, staging and production.

Sentiment analysis extracts the positive or negative score of a text

05/ Where can be applied?

A sentiment analysis application can be set to work on a wide variety of scenarios, for example:

  • Customer service inboxes or hotlines
  • Customer experience surveys and market research 
  • Support ticket systems
  • Quality control and product evaluations
  • Internal and external social media communication channels
  • Product and services review sites
  • Specialized forums for your industry and target groups 
  • Comment fields on product features launches
  • And more...

Uncover targeted insights on all use cases where a customer or end-users describes with words their experience with your services and products. 

Sentiment analysis can be implemented in a simple and effective way with minimum investment using the Peltarion platform. Contact us at contact@peltarion.com for guidance or check this tutorial for some inspiration: Is a movie review positive or negative?

  • Liliana Lindberg

    Liliana Lindberg

    Solutions Architect

    Liliana works as a solutions architect at Peltarion guiding customers to solve business challenges by successfully using AI and deep learning. She is passionate about emerging technologies and before joining Peltarion she worked for a number of years at Google as a customer engineer for the Google Cloud Platform team. Her academic background includes BSc. Systems and Computing Engineering; MSc. in Geographical Information Systems from the University of Calgary, Canada; and a Master’s level Business Leadership Specialization from Duke University.

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