Daylight getting started guide
Use this guide to kickstart your Daylight experience.
Luminoso Daylight is a text analytics application powered by QuickLearn®, Luminoso’s proprietary natural language modeling system. You don’t need custom ontology libraries or data science experience to use Daylight. As soon as you create a project, Daylight immediately begins analyzing data. Use Daylight to enhance the value of your unstructured natural language text through intelligent, automated analysis and quantification.
Extract actionable insights from what people are saying with Daylight. Organizations use Daylight to analyze text such as open-ended survey responses, product reviews, and support tickets. Using Daylight, they rapidly identify digital problems, understand concepts driving scores, and know which aspects of their products and services hold the strongest sentiment.
Use this guide to familiarize yourself with the basic functionality you’ll need to begin analyzing your data. Once you’re done reading, check out some of the other resources in the Luminoso Help Center.
Table of contents
- 1 Log in to Daylight
- 2 Preparing a dataset for upload
- 3 Uploading a dataset to create a new project
- 4 Start with the Highlights feature
- 5 Learn the sidebar
- 5.1 Search details
- 5.2 Active concepts
- 5.3 Filter documents
- 5.4 Configure visualization
- 5.5 Documents
- 5.5.1 Document viewer modal
- 5.5.2 Selecting metadata fields
- 5.5.3 Exporting
- 5.5.4 Creating a branched project
- 5.6 Export & share
- 5.7 Current View Box
- 5.7.1 Shared Views
- 6 Search bar and concept editor
- 7 Volume feature: Quantify concepts
- 8 Galaxy feature: Visualize associations
- 9 Drivers feature: Analyze mixed datasets
- 10 Sentiment feature: Detect positive, negative, and neutral
Log in to Daylight
Luminoso staff create new workspaces and users in Daylight. You will receive an email from provisioning@luminoso.com after your Luminoso Daylight account and user profile are created with an invitation to your new workspace. The link in this email expires after seven days. If your link expires or you can’t find this email after checking your spam folder, contact us at support@luminoso.com.
Once you have the email from provisioning@luminoso.com:
Click the link to access Daylight.
The Daylight login page appears.
Note: Use Google Chrome to access Daylight. Microsoft Edge is not a Daylight-compatible browser.Enter your username, which is automatically set as your email address.
Create a password and enter it a second time for validation.
Bookmark or save the login link for easy access.
Preparing a dataset for upload
Now that you have an account on Daylight and access to a Workspace, the next step is to upload some text data to analyze. You can refer to the Using different data sources with Daylight page for some pointers on different types of text data to consider. You will also want to include some metadata to provide context for the text data, such as demographic information, dates, scores, etc. Metadata can be used to filter the data to analyze a subset of the data set, and numerical data can be used in Drivers analysis, which you will learn about later in this document.
Key vocabulary
Before we get started, here are some terms that we will be using throughout this section of the document.
A verbatim is the conversational text component of the sample you have collected.
A document is a row of your source data, including the conversational text and any associated metadata.
Metadata is structured data that creates context for text responses. Metadata may include demographics, dates, scores, or product details.
A CSV file, or comma-separated value file, is a plain-text file format that is used to organize data. CSV files exclude styling information that is included in an Excel XLS or XLSX file formats. You can export a CSV file from most spreadsheet editors.
Data Fields
The following table describes the types of data that can be included in the data to be uploaded. The first row of the data will be used as the name of the data in that column. You will need to designate the data type for each column of the uploaded data during the uploading process. There are two ways to do this:
In your dataset file, make sure each header has the data type, then an underscore, then the column name. E.g. score_Rating, string_Location
As you upload your dataset file, you can change individual data types in Daylight.
Data type | Examples |
| Column header: text or text_[FieldName] |
|
|
Title | Column header: title or title_[FieldName] |
|
|
String | Column header: string_[FieldName] |
| Example: string_MemberLevel
|
Number | Column header: number_[FieldName] |
| Example: number_Age
|
Score | Column header: score_[FieldName] |
| Example: score_OverallExperience
|
Date | Column header: date_[FieldName] |
| Example: date_CheckoutDate ISO 8601 formatted dates:
US-style dates:
|
Sometimes, a metadata field can have multiple values within a single document. For example, a survey may ask the respondent “which of these products have you tried?”. In such a case, the respondent may select more than one product. There are two ways that you can format the data in such cases:
Have one column for this metadata field and enter all of the values separated with the “|” (pipe) character. In the example above, the column header can be “Products Used” and the value in a given cell could be “ProductA | ProductB | ProductC”.
Have multiple columns with the same column header name with a single value in each cell. In the example above, you would have as many columns as you need with the header name “Products Used” and populate each cell with a single Product. Some of the cells can be left blank.
In both cases, a single metadata field will be created with multiple values.
Supported languages and multilingual datasets
Daylight is capable of performing analysis natively in 15 languages. For best results with a multilingual dataset, split your data into one language per upload file. Each language will be uploaded and analyzed as its own Project.
Save as a CSV file
To upload your data to Daylight, save the file in CSV format, and make sure that the file extension is .csv. Daylight will also accept similarly formatted files, such as a tab separated value (TSV) file. In this case, make sure that the file extension is .tsv.
Uploading a dataset to create a new project
To upload a file and create a new Project, go to the Project page and click on the New project button on the top right corner of the page. You can get to the Project page by clicking the Luminoso logo or “Luminoso Daylight” on the top left of your screen. From this page, you can select a file to upload by clicking Choose file or by dragging and dropping a file onto the main panel on the page. Note that you can only upload 1 file at a time. Once a file has been selected, you will see a preview of your data which will look similar to the image below.
Document Usage Meter
The document usage indicator at the top of the page shows document usage relative to the contracted document allotment for your organization. Prior to selecting a file to upload, you will see the licensed document amount and the currently used document count. Once a file has been selected, you will additionally see the number of documents that the selected file will use out of the allotment.
Project Info section
The Project Info section consists of the Project name, Description, Workspace and Language.
Project name - defaults to the name of the uploaded file and can be edited
Description (optional) - can be used for information about the project
Workspace - defaults to whatever Workspace you were using when you clicked New project. If you have multiple Workspaces and prefer a different one, you can change it here.
Language - there is no default language selected. You must select one of the 15 available languages.