How to analyze sentiment of Discord Community Channels with generative AI

Sentiment Analysis

Discord

If you manage a Discord community channel, you know how important it is to understand your members and how they feel about your community. To take this understanding to the next level, you can use generative AI to analyze the sentiment of your Discord community channels. In this post, we’ll show you how to do just that.

What is Sentiment Analysis?

Sentiment analysis is an NLP technique that uses machine learning algorithms to identify and extract emotions expressed in a given piece of text. The algorithms are trained on labeled datasets of text samples with corresponding sentiment labels (positive, negative, or neutral). The model learns to recognize patterns and features in the text that are associated with different emotions, and uses these patterns to predict the sentiment of new, unseen text.

Sentiment analysis has many applications, from customer feedback analysis to social media monitoring, and market research. In this case, we are using it to understand how members of a Discord community channel feel about the community. It can help you identify areas for improvement, track changes in sentiment over time, and gain insights into your members' needs and preferences.

Example Use Cases

Here are some examples of use cases for sentiment analysis on Discord community channels:

Identify areas for improvement in community engagement and communication
Detect and address negative sentiment or concerns from members
Track changes in sentiment over time to measure the effectiveness of community initiatives

Teams that might find these use cases helpful include community managers, customer support, marketing, and operations.

Accessing your Data and Confirming Your Sentiment Scale

You can extract the data you need to analyze sentiment on Discord community channels using Discord's API or by exporting chat logs in CSV format. Once you have your data, you need to confirm the sentiment scale you will use for assessing community sentiment. Typically, sentiment is measured on a scale of -1 (most negative) to 1 (most positive) or with a sentiment rating scale like the one below:

Very Positive
Positive
Neutral
Negative
Very Negative

Once you have your data and sentiment scale, you can use generative AI to automatically assess the sentiment of your Discord community channels. This will help you improve the quality and consistency of your community engagement and ensure that your members feel heard and valued. It can also help you identify potential issues early on and prevent them from escalating.

Using AirOps to perform Keyword Identification

With AirOps, you can easily extract relevant keywords and phrases from your text-based data using the Keyword Identifier data app. Here's how:

Select "Keyword Identifier" from the Data Apps page. The input required for Keyword Identifier is the "text_field" which is the input text data.
Decide where you want the analysis to be performed and stored. The Keyword Identifier data app can be easily used in the AirOps Data App page and via API, but in this example, the analysis will be performed in Snowflake through an external function called AIROPS_KEYWORD_IDENTIFIER.

Here is an example SQL query:
```
SELECT
AIROPS_KEYWORD_IDENTIFIER(text_field) as result
FROM
your_table
```
Execute the keyword extraction analysis by running the SQL query. The output will contain an array of keywords and phrases extracted from the input text data.

Example Input:

"Hello, I am having trouble with my account. I cannot seem to log in and I have tried resetting my password multiple times."

Example Output:
```
"keywords": ["trouble", "account", "log in", "resetting", "password", "multiple times"],"summary": "A customer is having trouble logging into their account and has tried resetting their password multiple times."
```

Using AirOps to perform Sentiment Analysis

With AirOps, you can easily perform sentiment analysis on any text data such as reviews, support tickets, or sales calls using Sentiment Analyzer. Here’s how:

Select "Sentiment Analyzer" from the Data Apps page. The only input for Sentiment Analyzer is some text to analyze.
Decide where you want the analysis to be performed and stored. The Sentiment Analyzer data app can be easily used in the AirOps Data App page and via API, but in this example, the analysis will be performed in Snowflake through an external function called AIROPS_SENTIMENT_ANALYZER.

Here is an example SQL query:
```
SELECT
AIROPS_SENTIMENT_ANALYZER(text_field) as result
FROM
your_table
```
Execute the sentiment analysis by running the SQL query. The output will contain a sentiment score and sentiment summary, as well as a list of positive and negative keywords extracted from the input text data.
Input:
"I'm sorry to say that I had a terrible experience with your product. The customer service was unresponsive and the product didn't work as advertised."
Output:
```
"positive_keywords": [],"negative_keywords": ["terrible experience", "customer service", "unresponsive", "product", "didn't work", "advertised"],"score": -0.8,"sentiment": "Very Negative"
```

Using AirOps to perform Text Classification

With AirOps, you can easily perform classification using generative AI. Here’s how:

Select "Text Classifier'' from the Data Apps page. Below are the possible inputs for Text Classifier.text_field: The input text data.categories (optional): Categories can be specified as a comma-separated list. Leave empty for automatic determination.multi_category: Set to “true” if the text can belong to multiple categories, or “false” if it can only belong to one category.
Decide where you want the analysis to be performed and stored. The Text Classifier data app can be easily used in the AirOps Data App page and via API, but in this example, the analysis will be performed in Snowflake through an external function called AIROPS_CLASSIFIER.

Here is an example SQL query:
```
SELECT
AIROPS_CLASSIFIER(text_field, categories, multi_category) as result
FROM
your_table
```
Execute the classification analysis by running the SQL query. The output will contain a list of keywords extracted from the input text data that are relevant to the identified categories and a list of categories that the input text data belongs to based on the provided categories or automatic determination.

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