Get Started with Semantria Storage & Visualization
With Semantria Storage & Visualization, users can upload and manage databases of text, analyze that text with custom configurations, and report on results.
The main organizational concept is projects. Projects allow the user to unify feedback from multiple channels, like surveys, social media, chat logs, and call center transcripts. Users can combine multiple data sources into a project by uploading each source as a separate file. Once uploaded, users may group all related sources into an analysis group. Analysis groups allow for a 360° view of all available feedback.
Projects benefit from user management. Admins may invite teammates to work on projects. Once invited, an admin may grant a teammate read or write privileges. User management safeguards data privacy and dashboard integrity.
1. Create a project
Think of a project as a folder. It contains all relevant text data and analyses.
Data and analyses in projects can be easily shared between users in a Semantria account.
Understanding projects
A hotel chain has a project called Quarterly Reviews by Location. In this project there are .CSVs of unstructured TripAdvisor reviews for three hotel locations. By using projects, the user can combine these three unique data sources together by grouping them.
The user is able to generate graphs for all three locations in one dashboard for easy side-by-side comparison. Now the user sees that complaints about cleanliness are spiking in Hotel A, whereas Hotel B is receiving praise for cleanliness.
To create a project, click on the plus button on the top right of the projects page.


Creating projects is as simple as clicking a button
The user will be able to name the project, add a descriptive note to explain how the project will be used, and set an image for the project.
When the user creates a project, they are taken to that project page.


2. Create a collection and upload data
Projects are powered by data. Each time the user uploads data, they will put it in a collection. A collection can contain one data source and any number of analyses.
Any data source must be configured to have at a minimum two columns: an ID column and a Text column. The ID column provides a number that identifies each row of text for the Semantria API. Each row of text is know as a document.
Documents are simply individual calls to the Semantria API. Each call to the API costs one credit.
Meta data
Including meta data is optional, but doing so will unlock more reporting features. Users may include numerous columns of meta data to monitor demographics, location, dates, star rating, and more.
The user may also provide other fields (we support most Dublin Core fields, as well as any custom fields they want to provide).
To start the collection upload process, press the button that says "Upload Data."


First, the user is asked to name the collection.
Be sure to use only UTF-8 characters when naming collections and analyses.


After the user specifies a name they may upload a data source from Excel or JSON. Semantria Storage & Visualization will scan the file and identify any defined columns. Notice a drop-down menu appears when the user selects a column header field. This drop-down menu is pre-configured for common Dublin Core fields (e.g. creator, date, engagement, ID). These are only suggestions. The user may name each column header anything they wish.
Finally, the user may need to configure certain columns. Non-Dublin Core column headers require that the user specify the data type of the field by clicking configure under the column header.


Configuring data
When configuring non-Dublin Core column headers, the user will be prompted to select 1 of 4 field types:
- String: this is used to configure text data, where the column may contain titles to reviews, manager names, locations
- Integer: this is used to configure numerical data, where the column may contain customer IDs, star ratings, or product numbers
- Float: this is used to configure numerical data, where the column may contain decimals like prices or percentages
- Date: this is used to configure temporal data, where the column may contain dates. See the date section below for more information on accepted date formats.
It may take a minute or more for Semantria Storage & Visualization to upload a collection and prepare it for analysis.
Once the data has been uploaded and prepared, it may be analyzed with any of the Semantria configurations.
Accepted Date Formats
The rule of thumb for date formats is that SSV accepts all Excel date formats. If you are unsure if your CSV file has an acceptable date, open it in Excel and format the date as an Excel formatted date.
Format | Example |
---|---|
MM/dd/yy | 12/25/18 |
MM/dd/yy HH:mm | 12/25/18 21:04 |
MM/dd/yyyy | 12/25/2018 |
MM/dd/yyyy HH:mm | 12/15/2018 21:04 |
yyyy-MM-dd'T'HH:mm:ss | 2018-12-25T21:04:43 |
yyyy-MM-dd'T'HH:mm:ssX | 2018-12-25T21:04:43Z |
yyyy-MM-dd'T'HH:mm:ssX | 2018-12-25T21:04:43-0000 |
yyyy-MM-dd'T'HH:mm:ssX | 2018-12-25T21:04:43-00 |
yyyy-MM-dd'T'HH:mm:ssX | 2018-12-25T21:04:43-00:00 |
yyyyMMdd | 20181225 |
dd-MM-yyyy | 25-12-2018 |
yyyy-MM-dd | 2018-12-25 |
yyyy/MM/dd | 2018/12/25 |
dd MMM yyyy | 25 Dec 2018 |
dd MMMM yyyy | 25 December 2018 |
yyyyMMddHHmm | 20181225210443 |
yyyyMMdd HHmm | 20181225 2104 |
dd-MM-yyyy HH:mm | 25-12-2018 21:04 |
yyyy-MM-dd HH:mm | 2018-12-25 21:04 |
yyyy/MM/dd HH:mm | 2018/12/15 21:04 |
dd MMM yyyy HH:mm | 25 Dec 2018 21:04 |
dd MMMM yyyy HH:mm | 25 December 2018 21:04 |
yyyyMMddHHmmss | 20181225210443 |
yyyyMMdd HHmmss | 20181225 210443 |
dd-MM-yyyy HH:mm:ss | 25-12-2018 21:04:43 |
yyyy-MM-dd HH:mm:ss | 2018-12-25 21:04:43 |
MM/dd/yyyy HH:mm:ss | 12/15/2018 21:04:43 |
yyyy/MM/dd HH:mm:ss | 2018/12/15 21:04:43 |
dd MMM yyyy HH:mm:ss | 25 Dec 2018 21:04:43 |
dd MMMM yyyy HH:mm:ss | 25 December 2018 21:04:43 |
EEE, dd MMM yyyy HH:mm:ss z | Thu, 25 Dec 2018 21:04:43 UTC |
Updated about 1 year ago