Appen vs. IBM Watson Natural Language Understanding

It is well suited for the users and potential employee who are free of any job perspective and need their free time to be utilized. Users can use their free time to be used for submission of interesting tasks.

Whereas the number of tasks are very less and processing time is also very extensive and recruitment takes time more.

IBM Watson Natural Language Understanding is a Swiss Army knife that can be used in many scenarios. An extensive list of easy to use APIs is provided making it very easy to integrate it in any environment. The text analysis is decent and above market average. It generates results in many forms to suit may scenarios (important keywords, concepts, sentiment analysis, etc.).

Incentivized

• Project listing
• Hiring of the potential and qualified users
• Tracking of the projects

• Easy to use and extensive APIs.
• Decent accuracy.
• It recognizes concepts and semantic roles.

Incentivized

• Selection procedure is bit .
• The questionnaire need to be reviewed.

• Improve Sentiment Analysis accuracy.
• Prevent having conflicting results (sad and happy, etc.).
• Foreign names detection.

Incentivized

Appen offers projects mostly related to my native language and also according to my expertise . It offers very interesting projects to be completed , which requires not very expertise and less time to be completed for each task. It is also very convenient to use after selection for the task and also well rewarding against the time consumed for the task completion.

• It has Positive impact as it provides opportunity for new jobs in my area of expertise.

• Reduced development time.
• Increased solution efficiency in understanding the user.
• Increased solution scalability.

Incentivized