Make vs. Buy: Applications Exploiting Sentiment Analysis Services

In reviewing key end-of-year reports, articles and research summaries, a few stand out. Most notably, let’s look at Machine Learning as a Service: Part 1 (Sentiment analysis: 10 applications and 4 services) from Towards Data Science.

Two sections of that June 2018 report stand out:

1. What can I do with sentiment analysis?

This section sets the stage for coming up with potential enterprise use cases. It lists ten good examples from published academic research.

Actions: Ideate

  • Check the references in footnotes 4 through 13
  • Identify all the text streams your enterprise already captures somehow (and other related text streams you might also exploit, such as Twitter commentary that somehow relates to your business)
  • Given the text streams, which of the ten use cases might apply to your industry and business?
    • Don’t limit yourself to single use cases
      • How might you combine use cases to impact your business?
      • Consider both opportunity and threat scenarios
      • What happens to your business if a competitor emerges that is exploiting these services (or applications that depend on use of such services)?
      • Do not make technology implementation assumptions at this stage. (All four vendors listed in this research provide sentiment analysis services from “the cloud.” But there are non-cloud implementations if you need them. Park this issue as you conceptualize potential uses and experiment with the cloud based technology.)

2. What are some good sentiment analysis services?

This section is a straightforward snapshot of sentiment analysis services. It describes and evaluates Amazon, IBM, Google and Microsoft mid-2018 services. (Details will continue to evolve as the service providers enhance their offerings.)

Action: Experiment with the technology.

  • Assess the feasibility of the business scenarios you’ve envisioned above.
  • This is not the same thing as building the “solution” yourselves.

Most enterprises should not build:

  • The underlying Natural Language Processing technologies
  • Their own sentiment analysis services.
  • The production application that relies in whole or part on sentiment analysis services.

Most enterprises should:

  • Experiment to get a grasp of the feasibility of achieving their business objectives
  • Seek out specialist firms with demonstrated subject matter expertise in their industry
  • Follow the section on Five Easy Criterial To Seek described in our blog post on 5 Easy Criteria To Get Quick Returns on AI Investments

Disclosure: I am the author of this article and it expresses my own opinions. This is not a sponsored post. I have no vested interest in any of the entities mentioned in this post. Neither does the Analyst Syndicate.

Author: Tom Austin

Clearing the fog and misdirection in emerging technologies world wide. I founded TAG after over 40 years at Gartner, Inc. and DEC. Sought out by the press and executives world wide. In the most recent past, I was Gartner, Inc. lead for Artificial Intelligence research (2012-2018.) Deep background in brain sciences (biological constraints on learning.)

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