https://github.com/humansignal/brand-sentiment-analysis

Scripts utilizing Heartex platform to build brand sentiment analysis from the news
https://github.com/humansignal/brand-sentiment-analysis

lstm-sentiment-analysis natural-language-processing nlp nlp-machine-learning nlp-sentiment-classifier nlp-tutorial sentiment sentiment-analyser sentiment-analysis sentiment-classification tensorflow-text-classifiers transfer-learning

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Scripts utilizing Heartex platform to build brand sentiment analysis from the news

• Host: GitHub
• URL: https://github.com/humansignal/brand-sentiment-analysis
• Owner: HumanSignal
• License: apache-2.0
• Created: 2019-05-30T03:59:08.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2022-07-06T20:10:47.000Z (over 2 years ago)
• Last Synced: 2023-07-25T05:23:39.972Z (over 1 year ago)
• Topics: lstm-sentiment-analysis, natural-language-processing, nlp, nlp-machine-learning, nlp-sentiment-classifier, nlp-tutorial, sentiment, sentiment-analyser, sentiment-analysis, sentiment-classification, tensorflow-text-classifiers, transfer-learning
• Language: CSS
• Homepage: https://go.heartex.net/demo/sentiment/
• Size: 1.87 MB
• Stars: 21
• Watchers: 6
• Forks: 3
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# 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](https://heartex.net/use-case/sentiment)

![](https://github.com/heartexlabs/brand-sentiment-analysis/raw/master/demo.png)

# Installation 

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

link only!).

```sh

# install

python3 -m venv bsa-env

source bsa-env/bin/active

pip install -r requirements

```

```sh

# configure

export TOKEN=""

export BRAND=""

```

# Create Sentiment Model

```sh

# first we need to grab news data

python src/get_google_news.py --pages=10 --query=$BRAND --output=news.csv

```

```sh

#  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.

```sh

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

```

```sh

# 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.