SAS Visual Text Analytics Reviews

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.

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.

The main problem it solves is our inability to analyze at scale the large volume of unstructured text data we generate, such as comments on social media, open survey responses, and call center transcripts. Previously, this "voice of the customer" was a major blind spot.

.I like the user interface and the ability to compare different models that are available on the go along with their scaling

There is nothing much I dislike, but i have use a little more to go to the level of really getting something to dislike

Scaling and comparing different models

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

Very intitutive converting text into Analytics. Very good to convert all the CSR's into Analytics information.

More steps to remember and it needs more attention what is the end goals are

cool to look for any particular information in very large text files.

.Handling huge data base and data management analytical

Cost of the product and high level knowledge required

Analytical and reporting &

A good visualisation tool with connection to several different data sources made it much simpler to develop dashboards using census data and website logs.

Using values to sort that isn't found in a table or graphic. Reports and responses should have less delays.

Our BI dashboards, which let clients access updated data as soon as it becomes available, are made possible with SAS Visual Analytics. Furthermore, it enables them to export the data (in various forms) that underlies a chart (such as a bar chart, pie chart, etc.).

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.

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.

This tool helps us effortlessly create insights out of unstructured data. It is helping us save time as well as bandwidth. The prebuilt models are also helping us effortlessly understand the sentiments of our users.

The program is exceptionally robust, and visual analytics has a simple user interface and is very easy to use and comprehend.

The product is difficult to comprehend for new users since comprehensive documentation is unavailable.

It is mostly used for organising data and developing project flows. Additionally, it delivers a more accurate report output.

It is made to efficiently handle big data and readily extract insights from unstructured data on a wide scale.

Sometimes a custom scenario is challenging to create, lacks documentation, and cannot be used with various other datasets.

We use it to categorise sentiment-encoded output to analyse data and collect statistics, which helps us cope with massive amounts of textual data.

It has been quite helpful in locating the gaps in the data quality and improving our comprehension of the data from a business perspective.

There is a need for faster reporting and response times. The other components of the product are all excellent, therefore I have no complaints about any of them.

It may be used effectively to show complex and sophisticated statistics. Also, it allows us to analyse the data and discover links that were previously difficult to detect.