This project is a proof-of-concept demonstraing how you can use LLMs to perform competitive intelligence on customer reviews and feedback.

In this scenario, we're taking G2 reviews and performing topic modelling in a simple streamlit app.

The overall (processing) pipeline is as follows:

1. Get the G2 company reviews for your target companies (manual step, instructions below)
2. Basic data reshaping from resulting json (preprocess.py)
3. Split reviews into sentences
4. Embed sentences
5. Reduce dimensionality (slightly) and cluster sentences
6. Find N points close to the center of each cluster and stuff them in the LLM to extract the topics
7. Reduce dimensions to 2D in order to visualize

• Setting the OPENAI_API_KEY is mandatory
• If you want to fetch a new set of companies you need to set APIFY_API_TOKEN, otherwise, it will use the sample G2 reviews in the repo.

   git clone https://github.com/balmasi/g2_reviews_llm_topic_modeling

The easiest way to do this is to use Conda.

# Create the g2_reviews_topic_modeling_llm virtual environment
conda create -n g2_reviews_topic_modeling_llm python=3.10
# Activate the virtual environment
conda activate g2_reviews_topic_modelling_llm

pip install -r requirements.txt

1. Browse to your target G2 profiles to grab the slug from the url. For example https://www.g2.com/products/vena/reviews would be vena
2. Place each target company on a line in the data/slugs-to-fetch.txt file.
3. Set the APIFY_API_TOKEN in the .env file to your Apify API token
4. run the create_dataset.py command using python data/create_dataset.py

To run the app, execute the following command:

streamlit run streamlit_app.py

This will start the Streamlit server and launch the app in your default web browser.