Semantria Reviews

Reliable and Actionable Insights and Customizable Features.

Tuning is a quite different and the interface reporting is not very easy

Sentiment analysis of customers or leads acquired over time.

I can deploy it however I want like on premises, on cloud or even in hybrid. Also the machine learning processing the data is making my daily tasks super easy!

As I am still implementing it in the system so I don't have any dislikes.

The raw content to structured insights is really helping me out. Also the insights help me present significant data to the management.

The best thing about Semantria tool which I like most is its robust sentiments analysis capabilities. It excels in understanding and extracting sentiments from textual context and its one of the efficient tool I came across till now.

There is nothing much to dislike about Semantria but at initial level user may find issues in navigating across the tool so as per my advice there is little bit of work required on User experience.

Semantria solving many major problems regarding contexual text analysis like it benefit businesses and individuals by automating the process of extracting insights from large volumes of textual data.

The best I like about Semantria is test and sentiment analysis capabilities. It has high accuracy in sentiment analysis, entity recognition etc.

I feel customization will be difficult to users who don't have knowledge in Machine Learning.

It will aolve issues like Brand Monitoring, customer feedback analysis etc.

Semantria pays a way to learn more on deep learning of AI and the cloud architecture

Nothing all works well and everything is working fine

In analysis of the cloud architecture and in adoption of the next level Linux and networking.

I evaluated seven sentiment vendors, and found Semantria to be the best at scoring sentiment. This was for retail. In addition, I like that you can adjust the phrases to score the sentiment more positive or negative. Some phrases are clearly negative for some industries. The word "honor" for example may be deemed a positive word, but for retail, it's often in a negative context. With this engine, you can push this phrase along with another words that are within X proximity to be more negative. For example, "honor your price matching!" Or , "honor your agreements!", is often customers complaining in retail and you can polarize that to be more negative. I think their sentiment scoring is more accurate than their competitors.

The phrase/entity engine needs to be built out more.

We are able to get to customer complaints quickly and service those customers first.