Review table data model and metrics
Summary
TLDRThe video script discusses the Review BQ Schema, explaining how to view data in the BigQuery console after an export job. It covers various metrics captured at conversation, sentence, and word levels, such as topic names, sentiment scores, and language codes, with a full list available in resource 27.
Takeaways
- π The Review BQ Schema is discussed in this section, highlighting its importance in data analysis.
- π Once an Export job is completed, the resulting table is accessible in the BQ console for further examination.
- π Users can preview data directly from the BQ console or utilize the SQL query editor for a more in-depth look.
- π SQL queries can be written within the SQL workspace to view data, offering flexibility in data retrieval.
- π£οΈ Conversation-level metrics are captured, providing insights into various aspects of interactions.
- π Metrics like topic name, silence percentage, and entities identified are part of the conversation-level data.
- π Sentence-level metrics are also captured, including sentiment scores, intent, and highlights, offering detailed sentiment analysis.
- π A positive sentiment score indicates a more favorable sentiment towards the conversation.
- π£οΈ Word-level metrics such as speaker tag and language code are exported, adding granularity to the data.
- π For a comprehensive list of all exported metrics, reference resource 27 mentioned in the course.
Q & A
What is the purpose of the Review BQ Schema?
-The Review BQ Schema is used to organize and manage data exports in BigQuery, making it easier to analyze and review various conversation metrics.
How can you access the exported table in BigQuery?
-Once the export job is completed, the table becomes visible in the BigQuery console, accessible for further analysis.
What is the function of the 'Preview' button in BigQuery?
-The 'Preview' button allows users to view a sample of the data directly in the BigQuery console without needing to run a full query.
How can users interact with the data in BigQuery?
-Users can interact with the data using the SQL query editor or by writing SQL queries directly in the SQL workspace.
What types of metrics are captured at the conversation level?
-Metrics captured at the conversation level include topic name, silence percentage of user or agents, and entities identified from the conversation.
What does the silence percentage metric represent?
-The silence percentage metric indicates the proportion of time during a conversation where there is no speech, either by the user or the agent.
What metrics are captured at the sentence level in the Review BQ Schema?
-At the sentence level, metrics such as sentiment score, intent, and highlights are captured.
What does a higher sentiment score indicate?
-A higher sentiment score indicates a more positive sentiment in the analyzed sentence.
What word-level metrics are exported in the Review BQ Schema?
-Word-level metrics like speaker tag and language code are exported.
Where can one find a full list of exported metrics?
-A full list of exported metrics can be found in resource 27 referenced at the end of the course.
How can SQL queries be used to analyze the data in BigQuery?
-SQL queries can be used to filter, aggregate, and analyze the data in BigQuery, providing insights into various aspects of the conversations.
Outlines
π Review BQ Schema and Data Exploration
This paragraph introduces the Review BQ Schema, detailing the process of exporting data and its visibility in the BQ console. It explains the various ways to view the data, such as previewing it or using the SQL query editor. The paragraph also highlights the different metrics captured at conversation and sentence levels, including topic names, silence percentages, sentiment scores, intents, and highlights. Additionally, it mentions word-level metrics like speaker tags and language codes, with a reference to resource 27 for a complete list of exported metrics.
Mindmap
Keywords
π‘Review BQ Schema
π‘Export job
π‘BQ console
π‘Preview
π‘SQL query editor
π‘SQL workspace
π‘Conversation level metrics
π‘Silence percentage
π‘Entities
π‘Sentence level metrics
π‘Sentiment score
π‘Speaker tag
π‘Language code
π‘Resource 27
Highlights
The Export job completion makes the table visible in the BQ console.
Preview feature allows viewing data directly in the BQ console.
SQL query editor can be utilized to interact with the data.
Data can be viewed directly inside the SQL workspace by writing an SQL query.
Multiple metrics are captured at the conversation level.
Metrics include topic name, silence percentage, and entities identified.
Sentence level metrics are also captured.
Sentiment score indicates the positivity of a sentence.
Intent and highlights are captured at the sentence level.
Word level metrics such as speaker tag and language code are exported.
A full list of exported metrics is available in resource 27.
The BQ console provides a comprehensive view of conversation data.
SQL queries can be used to manipulate and analyze conversation data.
Conversation metrics help in understanding the dynamics of interactions.
Sentence level analysis provides insights into the sentiment and intent of conversations.
Word level data offers detailed information about the language and speakers.
Resource 27 is a valuable reference for understanding the scope of exported metrics.
The BQ console is an essential tool for data visualization and analysis.
SQL workspace enables direct interaction with conversation data.
Transcripts
In this section, weβll discuss Review BQ Schema.
Once the Export job is completed, the table
is visible in the BQ console.
We can also click Preview to view the data or make use of the SQL query editor.
Alternatively, we can view the data directly inside the SQL workspace by writing an SQL
query.
Many different metrics are captured at conversation level.
Some of these metrics include: topic name,
silence percentage of user or agents, and entities identified from the conversation.
There are also multiple metrics captured at the sentences level, such as:
the sentiment score of a sentence where a more positive score indicates positive sentiment,
intent and highlights.
And Word level metrics like speaker tag and language code are also exported.
For a full list of exported metrics, refer to resource 27 referenced at the end of this
course.
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