Obsei: OBserve, SEgment and Inform

Obsei is intended to be a workflow automation tool for text segmentation need. Obsei consist of -

OBserver, observes platform like Twitter, Facebook, App Stores, Google reviews, Amazon reviews and feed that information to,
SEgmenter, which perform text classification and sentiment analysis and feed that information to,
Informer, which send it to ticketing system, data store or other places for further action and analysis.

Current flow -

A future concept (Coming Soon! 🙂)

Installation

To use as SDK

Install via PyPi:

pip install obsei

Install from master branch (if you want to try the latest features):

git clone https://github.com/lalitpagaria/obsei.git
cd obsei
pip install --editable .

To update your installation, just do a git pull. The --editable flag will update changes immediately.

To use as Rest interface

Start docker with default configuration file:

docker run -d --name obesi -p 9898:9898 lalitpagaria/obsei:latest

Start docker with custom configuration file (Assuming you have configfile config.yaml at /home/user/obsei/config at host machine):

docker run -d --name obesi -v "/home/user/obsei/config:/home/user/config" -e "OBSEI_CONFIG_PATH=/home/user/config" -e "OBSEI_CONFIG_FILENAME=config.yaml" -p 9898:9898 lalitpagaria/obsei:latest

Start docker locally with docker-compose:

docker-compose up --build

Following environment variables are useful to customize various parameters -

OBSEI_CONFIG_PATH: Configuration file path (default: ../config)
OBSEI_CONFIG_FILENAME: Configuration file name (default: rest.yaml)
OBSEI_NUM_OF_WORKERS: Number of workers for rest API server (default: 1)
OBSEI_WORKER_TIMEOUT: Worker idle timeout in seconds (default: 180)
OBSEI_SERVER_PORT: Rest API server port (default: 9898)
OBSEI_WORKER_TYPE: Gunicorn worker type (default: uvicorn.workers.UvicornWorker)

Use cases

Obsei use cases are following, but not limited to -

Automatic customer issue ticketing based on sentiment analysis
Proper tagging of ticket like login issue, signup issue, delivery issue etc for faster disposal
Checking effectiveness of social media marketing campaign
Extraction of deeper insight from feedbacks on various platforms
Research purpose

Components and Integrations

Source/Observer: Twitter, Play Store Reviews (Facebook, Instagram, Google reviews, Amazon reviews, App Store reviews, Slack, Microsoft Team, Chat-bots etc planned in future)
Analyzer/Segmenter: Sentiment and Text classification (QA, Natural Search, FAQ, Summarization etc planned in future)
Sink/Informer: HTTP API, ElasticSearch, DailyGet, and Jira (Salesforce, Zendesk, Hubspot, Slack, Microsoft Team, etc planned in future)
Processor/WorkflowEngine: Simple integration between Source, Analyser and Sink (Rich workflows using rule engine planned in future)
Convertor: Very important part, which convert data from analyzer format to the format sink understand. It is very helpful in any customizations, refer dailyget_sink.py and jira_sink.py.

Note: In order to use some integrations you would need credentials, refer following list -

Twitter: To make authorized API call, get access from dev portal. Read about search api for more details.
Play Store: To make authorized API calls, get service account's credentials. Read about review api for more details.

Examples and Screenshots

Refer example and config folders for obsei usage and configurations.

Jira

Attribution

This could not have been possible without following open source software -

searchtweets-v2: For Twitter's API v2 wrapper
vaderSentiment: For rule-based sentiment analysis
transformers: For text-classification pipeline
tweet-preprocessor: For tweets preprocessing and cleaning
atlassian-python-api: To interact with Jira
elasticsearch: To interact with Elasticsearch
hydra: To elegantly configuring Obsei
apscheduler: To schedule task to execute desired workflow
pydantic: For data validation
sqlalchemy: As SQL toolkit to access DB storage
fastapi & gunicorn: For HTTP server and API interface

Contribution

Currently, we are not accepting any pull requests. If you want a feature or something doesn't work, please create an issue.

Citing Obsei

If you use obsei in your research please use the following BibTeX entry:

@Misc{Pagaria2020Obsei,
  author =       {Lalit Pagaria},
  title =        {Obsei - A workflow automation tool for text segmentation need},
  howpublished = {Github},
  year =         {2020},
  url =          {https://github.com/lalitpagaria/obsei}
}

Acknowledgement

We would like to thank DailyGet for continuous support and encouragement. Please check DailyGet out. it is a platform which can easily be configured to solve any business process automation requirements.

rajagurunath / obsei