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[text analytics] add output to opinion mining sample #13494

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Sep 1, 2020
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[text analytics] add output to opinion mining sample #13494

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51 changes: 51 additions & 0 deletions ...textanalytics/samples/async_samples/sample_analyze_sentiment_with_opinion_mining_async.py
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Original file line number Diff line number Diff line change
Expand Up @@ -24,6 +24,57 @@
Set the environment variables with your own values before running the sample:
1) AZURE_TEXT_ANALYTICS_ENDPOINT - the endpoint to your Cognitive Services resource.
2) AZURE_TEXT_ANALYTICS_KEY - your Text Analytics subscription key

OUTPUT:
In this sample we will be combing through the reviews of a potential hotel to stay at: Hotel Foo.
I first found a handful of reviews for Hotel Foo. Let's see if I want to stay here.


Let's see how many positive and negative reviews of this hotel I have right now
...We have 3 positive reviews and 2 negative reviews.

Looks more positive than negative, but still pretty mixed, so I'm going to drill deeper into the opinions of individual aspects of this hotel

In order to do that, I'm going to sort them based on whether these opinions are positive, mixed, or negative


Let's look at the 7 positive opinions users have expressed for aspects of this hotel
...Reviewers have the following opinions for the overall positive 'concierge' aspect of the hotel
......'positive' opinion 'nice'
...Reviewers have the following opinions for the overall positive 'AC' aspect of the hotel
......'positive' opinion 'good'
......'positive' opinion 'quiet'
...Reviewers have the following opinions for the overall positive 'breakfast' aspect of the hotel
......'positive' opinion 'good'
...Reviewers have the following opinions for the overall positive 'hotel' aspect of the hotel
......'positive' opinion 'good'
...Reviewers have the following opinions for the overall positive 'breakfast' aspect of the hotel
......'positive' opinion 'nice'
...Reviewers have the following opinions for the overall positive 'shuttle service' aspect of the hotel
......'positive' opinion 'loved'
...Reviewers have the following opinions for the overall positive 'view' aspect of the hotel
......'positive' opinion 'great'
......'positive' opinion 'unobstructed'


Now let's look at the 1 mixed opinions users have expressed for aspects of this hotel
...Reviewers have the following opinions for the overall mixed 'rooms' aspect of the hotel
......'positive' opinion 'beautiful'
......'negative' opinion 'dirty'


Finally, let's see the 4 negative opinions users have expressed for aspects of this hotel
...Reviewers have the following opinions for the overall negative 'food' aspect of the hotel
......'negative' opinion 'unacceptable'
...Reviewers have the following opinions for the overall negative 'service' aspect of the hotel
......'negative' opinion 'unacceptable'
...Reviewers have the following opinions for the overall negative 'elevator' aspect of the hotel
......'negative' opinion 'broken'
...Reviewers have the following opinions for the overall negative 'toilet' aspect of the hotel
......'negative' opinion 'smelly'


Looking at the breakdown, even though there were more positive opinions of this hotel, I care the most about the food and the toilets in a hotel, so I will be staying elsewhere
"""

import os
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51 changes: 51 additions & 0 deletions ...tanalytics/azure-ai-textanalytics/samples/sample_analyze_sentiment_with_opinion_mining.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -24,6 +24,57 @@
Set the environment variables with your own values before running the sample:
1) AZURE_TEXT_ANALYTICS_ENDPOINT - the endpoint to your Cognitive Services resource.
2) AZURE_TEXT_ANALYTICS_KEY - your Text Analytics subscription key

OUTPUT:
In this sample we will be combing through the reviews of a potential hotel to stay at: Hotel Foo.
I first found a handful of reviews for Hotel Foo. Let's see if I want to stay here.


Let's see how many positive and negative reviews of this hotel I have right now
...We have 3 positive reviews and 2 negative reviews.

Looks more positive than negative, but still pretty mixed, so I'm going to drill deeper into the opinions of individual aspects of this hotel

In order to do that, I'm going to sort them based on whether these opinions are positive, mixed, or negative


Let's look at the 7 positive opinions users have expressed for aspects of this hotel
...Reviewers have the following opinions for the overall positive 'concierge' aspect of the hotel
......'positive' opinion 'nice'
...Reviewers have the following opinions for the overall positive 'AC' aspect of the hotel
......'positive' opinion 'good'
......'positive' opinion 'quiet'
...Reviewers have the following opinions for the overall positive 'breakfast' aspect of the hotel
......'positive' opinion 'good'
...Reviewers have the following opinions for the overall positive 'hotel' aspect of the hotel
......'positive' opinion 'good'
...Reviewers have the following opinions for the overall positive 'breakfast' aspect of the hotel
......'positive' opinion 'nice'
...Reviewers have the following opinions for the overall positive 'shuttle service' aspect of the hotel
......'positive' opinion 'loved'
...Reviewers have the following opinions for the overall positive 'view' aspect of the hotel
......'positive' opinion 'great'
......'positive' opinion 'unobstructed'


Now let's look at the 1 mixed opinions users have expressed for aspects of this hotel
...Reviewers have the following opinions for the overall mixed 'rooms' aspect of the hotel
......'positive' opinion 'beautiful'
......'negative' opinion 'dirty'


Finally, let's see the 4 negative opinions users have expressed for aspects of this hotel
...Reviewers have the following opinions for the overall negative 'food' aspect of the hotel
......'negative' opinion 'unacceptable'
...Reviewers have the following opinions for the overall negative 'service' aspect of the hotel
......'negative' opinion 'unacceptable'
...Reviewers have the following opinions for the overall negative 'elevator' aspect of the hotel
......'negative' opinion 'broken'
...Reviewers have the following opinions for the overall negative 'toilet' aspect of the hotel
......'negative' opinion 'smelly'


Looking at the breakdown, even though there were more positive opinions of this hotel, I care the most about the food and the toilets in a hotel, so I will be staying elsewhere
"""

import os
Expand Down