Brand Sentiment

A set of scripts that makes sentiment analysis of your brand based on Google News and Twitter news streams. It utilizes Heartex platform to create a custom neural network to do the study specifically for your brand

Tutorial

Installation

Important. To make it work you need to obtain Heartex token, to do so signup here. We give you a free account with 10k API requests (with above link only!).

# install
python3 -m venv bsa-env
source bsa-env/bin/active
pip install -r requirements

# configure
export TOKEN=""
export BRAND=""

Create Sentiment Model

# first we need to grab news data
python src/get_google_news.py --pages=10 --query=$BRAND --output=news.csv

#  create project on heartex
python src/create_sentiment_project.py --token=$TOKEN --input=news.csv

# you will get project id, save it here
export SENTIMENT_PROJECT_ID=""

Open up src/config.json and put $TOKEN and $SENTIMENT_PROJECT_ID there

Run

Execute python3 service.py config.json

Add your own data

[TBD]

Advanced: Filter Results

In case your brand may appear in different contexts, for example, with the name of one of your products (ex: Apple Watch), you may want to filter those occurrences first.

To do that we will use another type of model which is called a tagger model. It learns when you tag relevant occurrences.

PRODUCTS="Apple,iOS,iPadOS,watchOS,macOS,MacPro,Pro Display"

# create Heartex project to filter news that are only relevent to your brand name

# you will get back a link where you need to train a neural network a little bit to make it understand what is relevent to you
python src/create_filter_project.py --token=$TOKEN --input=news.csv --labels=$PRODUCTS

# set project here
export FILTER_PROJECT=""

Now you have what is called a smart filter, edit config.json and include it there. You will see smart filter buttons on the index page.