How to analyze sentiment of TikTok Social Media Posts with generative AI

Sentiment Analysis

TikTok

As a social media manager, it's important to understand how your audience feels about your brand and content. Sentiment analysis can provide valuable insights into the emotions and opinions expressed in TikTok posts related to your brand. In this post, we'll show you how to use generative AI to automatically perform sentiment analysis on TikTok social media posts.

What is Sentiment Analysis?

Sentiment analysis is a natural language processing (NLP) technique that involves using machine learning algorithms to automatically identify and extract the emotions or opinions expressed in a given piece of text.

For example, sentiment analysis could be used to determine whether a TikTok post is positive, negative, or neutral. This could be useful for identifying trends in user feedback, monitoring brand sentiment, and improving engagement with your audience.

Example Use Cases

Some use cases for performing sentiment analysis on TikTok social media posts include:

Monitoring brand sentiment
Identifying trends in user feedback
Improving engagement with your audience
Tracking the success of marketing campaigns

Teams that might find these use cases helpful include: social media, marketing, product, and operations.

Accessing Your Data and Confirming Your Sentiment Scale

To analyze sentiment in TikTok posts, you'll need to access data related to your brand or content. You can extract this data using TikTok's API, export it in CSV format, or use a third-party tool that specializes in social media data analysis.

Once you have your data, you'll need to confirm the sentiment scale you'll use for assessing TikTok posts. Typically, sentiment is measured on a scale of -1 (most negative) to 1 (most positive). You can also assign sentiment ratings such as "Very Positive", "Positive", "Neutral", "Negative", and "Very Negative".

For example, a TikTok post that features your brand in a positive light might be assigned a sentiment rating of "Very Positive" or "Positive". A post that criticizes your brand might be assigned a rating of "Negative" or "Very Negative".

Once you have your data and sentiment scale, you can use generative AI to automatically assess the sentiment of your TikTok social media posts. This will help you improve your understanding of your audience and optimize your social media strategy.

By analyzing the sentiment of your TikTok posts, you can gain valuable insights into how your audience feels about your brand and content. This can help you identify areas for improvement, engage with your audience more effectively, and ultimately drive more engagement and revenue for your business.

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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