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

http://heartex.net/

Installation

! Important. To make it work you need to obtain Heartex token, to do so [http://heartex.net/business/signup/?ref=github](register here). We give you a free account with 10k API requests (with above link only!).

# install

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

Run it for your own brand

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

# 
python src/create_sentiment_project.py --token=$TOKEN --input=news.csv

export SENTIMENT_PROJECT=""

# predict dataset and save it's sentiment 
python src/add_sentiment_chart.py --token=$TOKEN --project=$SENTIMENT_PROJECT --input=news.csv --results=$BRAND

# open sentiment html
firefox $BRAND/index.html

Filter Results First

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=""

# get predictions
python src/predict_and_filter.py --project=$FILTER_PROJECT --token=$TOKEN --output=filtered.csv --filter-labels=$PRODUCTS

Now you have filtered.csv which you can use for further sentiment analysis.