SAS Visual Text Analytics Reviews & Product Details

What impresses me the most is the visual interface based on pipelines, which demystifies complex NLP processes. The ability to build concept extraction models, sentiment analysis, and topic categorization interactively greatly accelerates development. The native integration with the rest of the SAS Viya ecosystem is a huge differentiator, allowing the insights extracted from the text to be easily used in dashboards in Visual Analytics or as variables in predictive models. Review collected by and hosted on G2.com.

The cost of licensing is, without a doubt, the biggest downside, being a considerable investment. Additionally, the customization of more advanced rules (using LITI syntax) has a steep learning curve and may seem less flexible for those coming from an open-source programming background with Python. The consumption of computational resources can also be quite high when processing large volumes of data. Review collected by and hosted on G2.com.

I like the user interface and the ability to compare different models that are available on the go along with their scaling Review collected by and hosted on G2.com.

There is nothing much I dislike, but i have use a little more to go to the level of really getting something to dislike Review collected by and hosted on G2.com.

Interactive Visualizations: SAS Visual Text Analytics offers interactive visualizations that allow users to explore and understand the results of their text analyses. Visual representations such as word clouds, heat maps, bar charts, and network diagrams make it easier to communicate findings and identify key insights. Review collected by and hosted on G2.com.

- The pricing structure may not be suitable for all budgets.

- Advanced users or organizations with unique requirements may need to rely on additional programming or customization options to extend the capabilities of the software. Review collected by and hosted on G2.com.

This tool helps me effortlessly create insights from unstructured data. It has a user-friendly interface which is a plus. It has prebuilt natural language models which help us effortlessly extract information like context, sentiments etc. Review collected by and hosted on G2.com.

Although SAS Visual Text Analytics is very user-friendly, it cannot be customised for wide range of resources. Another thing which I dislike is it is not open-sourced(which means there is less help from the community, and the scope of the discussion forum is limited). With minimal learning materials available on the internet, it becomes problematic when you are struck somewhere. However, this will improve as the user base increase over time. Review collected by and hosted on G2.com.

SAS Visual Text Analytics is a pre-built package in the form of Nodes to get the required output like topics, concepts, sentiments and relations between the text to categorize the content.

Upsides:

* Easy to import unstructured data of various types and access them for analysis

*GUI-based application, which is easy for all the users in the respective domain can learn and use for either research or analysis or for the purpose intended Review collected by and hosted on G2.com.

It is not an open source, and learning material is not widely available on the internet. Only People with a license or having partnered with SAS gets to learn. Even though Custom modification of the node (Packages) is possible, it cannot be customized for a wide range of Resources Review collected by and hosted on G2.com.

SAS Visual Text Analytics helps create value from large textual datasets by extracting information like key relationships, sentiments, context etc. It offers analysis nodes for text parsing, concepts, topics and categories, using which it can customise the model to our needs. SAS VTA is a web-based application, and the user interface is natural and can be used to perform a complete set of operations like model deployment and automation. Review collected by and hosted on G2.com.

SAS Visual Text Analytics struggles with multilingual text, and it isn't easy to build custom models that can vary according to the domain. I tried to upload a custom Tokenizer, but I was not successful. SAP has limited help from community as it is not an open-source tool. Review collected by and hosted on G2.com.

SAS Visual Text Analytics has many built-in natural language models, which are available as Nodes in the platform and can use to identify information like context, sentiment, topics or key terms in textual data.

This tool can help skim through unstructured data to create insights from large documents.

The device has a user-friendly interface and can be exploited to eliminate human effort. Review collected by and hosted on G2.com.

SAS Visual Text Analytics has a smooth user experience. It offers fantastic potential, but this tool does not have details of the inner workings of the models running in the background. The platform lacks proper documentation about the models used for inference as this is a licensed tool. Review collected by and hosted on G2.com.

A good visualisation tool with connection to several different data sources made it much simpler to develop dashboards using census data and website logs. Review collected by and hosted on G2.com.

Using values to sort that isn't found in a table or graphic. Reports and responses should have less delays. Review collected by and hosted on G2.com.

Handling huge data base and data management analytical Review collected by and hosted on G2.com.

Cost of the product and high level knowledge required Review collected by and hosted on G2.com.

Very intitutive converting text into Analytics. Very good to convert all the CSR's into Analytics information. Review collected by and hosted on G2.com.

More steps to remember and it needs more attention what is the end goals are Review collected by and hosted on G2.com.