Unlocking Insights with Azure Sentiment Analysis

Azure Sentiment Analysis is a powerful tool that can help you understand customer opinions and emotions from text data. By analyzing large volumes of text, you can gain valuable insights into customer satisfaction, preferences, and pain points.

With Azure Sentiment Analysis, you can analyze text data from various sources, including social media, customer feedback forms, and product reviews. This allows you to get a comprehensive view of customer opinions and emotions.

By leveraging Azure Sentiment Analysis, you can identify trends and patterns in customer feedback, which can inform business decisions and drive improvement initiatives. For example, if you notice a high percentage of negative reviews for a particular product feature, you can use this information to prioritize changes and enhancements.

Azure AI Language is a powerful tool that can do some amazing things. It can detect the language of a given text, which is super helpful when working with texts from different parts of the world.

You can use Azure AI Language for language detection, key phrase extraction, sentiment analysis, named entity recognition, and entity linking. These capabilities are all designed to help you extract information from text.

Language detection is a key feature of Azure AI Language, allowing you to identify the language of a given text. This can be really useful when working with texts from different languages.

Here are the five capabilities of Azure AI Language:

Sentiment analysis is a really powerful tool that can help you understand the emotions behind a piece of text. By using Azure AI Language, you can quickly and easily determine whether a text is positive or negative.

Sentiment Analysis is a powerful tool that helps us determine whether a text input or document is positive or negative. It's particularly useful for tasks like evaluating restaurant reviews, customer feedback, or social media posts.

Azure AI Language can perform Sentiment Analysis, providing a sentiment score for the overall document and individual sentence sentiment for each document submitted. The response includes confidence scores between 0 and 1, with higher scores indicating greater confidence in the sentiment.

You can use Sentiment Analysis in Power BI using Azure Cognitive Services, which provides cloud-based AI capabilities like text analytics. This includes Sentiment Analysis and keyphrase extraction.

The accuracy of Sentiment Analysis can be improved by combining services. For example, combining Google Cloud Natural Language, AWS Comprehend, and IBM Watson achieved an accuracy of 73%. Azure Text Analytics, however, performed to an accuracy of 72% on the same data set.

To implement Sentiment Analysis in Power BI, you'll need to link your Azure AI service and select the language and text column for analysis. The service will then provide sentiment labels and confidence scores for each document and sentence.

Here's a summary of the sentiment analysis features:

Machine learning can also be used to improve the accuracy of Sentiment Analysis. For example, the Ranger package, an implementation of Random Forest, achieved an average accuracy of 78% when used to combine the results of four sentiment analysis services.

Creating a custom sentiment analysis project in Azure involves a clear and structured approach. To get the most out of your model, you need to define your schema, which means knowing your data and identifying the sentiments you want to analyze.

Defining your schema is crucial to avoid ambiguity in your data, and it's essential to label your data properly to ensure the quality of your model's performance. This involves making sure that your sentiments are clearly separable from each other.

Here are the key steps to follow in the project development lifecycle:

You can integrate Azure Cognitive Services with Power BI to unlock its full potential.

Azure Cognitive Services provide cloud-based AI capabilities, including text analytics like Sentiment Analysis and keyphrase extraction.

To implement a basic Sentiment Analysis pipeline, you can use Azure Cognitive Services.

Its documentation can be found here.

This integration allows you to leverage the power of AI in your Power BI reports and dashboards.

To get the most out of your custom sentiment analysis project, you need to follow a typical workflow.

First, create an Azure AI Language resource, which grants you access to the features offered by Azure AI Language. It generates a password (called a key) and an endpoint URL that you use to authenticate API requests.

Next, create a request using either the REST API or the client library for C#, Java, JavaScript, and Python. You can also send asynchronous calls with a batch request to combine API requests for multiple features into a single call.

To send the request, include your text data and authenticate with your key and endpoint.

Here are the steps in a concise list:

You can create custom functions in Azure Sentiment Analysis to perform more complex tasks, such as analyzing sentiment in a specific language or domain.

Azure Sentiment Analysis offers pre-built models for 11 languages, including English, Spanish, French, and Chinese.

By using custom functions, you can also analyze sentiment in text data that doesn't fit into the pre-built models, such as text with sarcasm or idioms.

Renaming a custom function is a good idea, as it makes it easier to understand what the function does. Let's rename our custom function as SA, short for Sentiment Analysis.

To work with our data, we need to promote the first row as a header by selecting "Use First Row as Headers" in the home tab.

Changing the data type of the columns appropriately is also important. This will ensure that our data is accurate and reliable.

To invoke the SA custom function, select the "Comment" column and then click "Add Column" and then "Invoke Custom Function".

We now have the Sentiment classification by Azure Text Analytics for each row, which can be Positive, Negative, Neutral, or Mixed.

The confidence score for each classification per row ranges from 0 (minimum) to 1 (maximum).

For example, the first row is classified as having a “negative” sentiment with a confidence score of 100%.

Similarly, the 9th row has been classified as having “Mixed” sentiments with a confidence score of 67% in it being a positive comment and 33% as a negative comment.

The description of the labels is as below:

We can add a conditional column to get a single confidence score corresponding to the predicted classification.

This can be done by simply assigning a confidence score of 0 to all classifications with a “Mixed” outcome in the simplest implementation.