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Obsei is intended to be an automation tool for text analysis need. Obsei consist of -

  • Observer, observes platform like Twitter, Facebook, App Stores, Google reviews, Amazon reviews etc and feed that information to,
  • Analyzer, which perform text analysis like classification, sentiment, translation, PII etc and feed that information to,
  • Informer, which send it to ticketing system, data store etc for further action and analysis.

Current flow -

A future concept (Coming Soon! 🙂)


For detailed installation instructions, usages and example refer documentation.

How to use

To try in Colab Notebook click: Colab

To try in Binder click: Binder

Expend following steps and create your workflow -

Step 1: Prerequisite

Install following if system do not have -

Step 2: Install Obsei

Install via PyPi:

pip install obsei

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

git clone
cd obsei
pip install --editable .

NOTE: On Windows you have to install pytorch manually. Refer

Step 3: Configure Source/Observer
from obsei.source.twitter_source import TwitterCredentials, TwitterSource, TwitterSourceConfig

# initialize twitter source config
source_config = TwitterSourceConfig(
   keywords=["issue"], # Keywords, @user or #hashtags
   lookup_period="1h", # Lookup period from current time, format: `<number><d|h|m>` (day|hour|minute)
       # Enter your twitter consumer key and secret. Get it from

# initialize tweets retriever
source = TwitterSource()
from obsei.source.email_source import EmailConfig, EmailCredInfo, EmailSource

# initialize email source config
source_config = EmailConfig(
   # List of IMAP servers for most commonly used email providers
   # Also, if you're using a Gmail account then make sure you allow less secure apps on your account -
   # Also enable IMAP access -
   imap_server="", # Enter IMAP server
       # Enter your email account username and password
   lookup_period="1h" # Lookup period from current time, format: `<number><d|h|m>` (day|hour|minute)

# initialize email retriever
source = EmailSource()
AppStore Reviews Scrapper
from obsei.source.appstore_scrapper import AppStoreScrapperConfig, AppStoreScrapperSource

# initialize app store source config
source_config = AppStoreScrapperConfig(
   # Need two parameters app_id and country. 
   # `app_id` can be found at the end of the url of app in app store. 
   # For example -
   # `310633997` is the app_id for xcode and `us` is country.
   lookup_period="1h" # Lookup period from current time, format: `<number><d|h|m>` (day|hour|minute)

# initialize app store reviews retriever
source = AppStoreScrapperSource()
Play Store Reviews Scrapper
from obsei.source.playstore_scrapper import PlayStoreScrapperConfig, PlayStoreScrapperSource

# initialize play store source config
source_config = PlayStoreScrapperConfig(
   # Need two parameters package_name and country. 
   # `package_name` can be found at the end of the url of app in play store. 
   # For example -
   # `` is the package_name for xcode and `us` is country.
   lookup_period="1h" # Lookup period from current time, format: `<number><d|h|m>` (day|hour|minute)

# initialize play store reviews retriever
source = PlayStoreScrapperSource()
from obsei.source.reddit_source import RedditConfig, RedditSource, RedditCredInfo

# initialize reddit source config
source_config = RedditConfig(
   subreddits=["wallstreetbets"], # List of subreddits
   # Reddit account username and password
   # You can also enter reddit client_id and client_secret or refresh_token
   # Create credential at
   # Also refer
   # Currently Password Flow, Read Only Mode and Saved Refresh Token Mode are supported
   lookup_period="1h" # Lookup period from current time, format: `<number><d|h|m>` (day|hour|minute)

# initialize reddit retriever
source = RedditSource()
Reddit Scrapper

Note: Reddit heavily rate limit scrappers, hence use it to fetch small data during long period

from obsei.source.reddit_scrapper import RedditScrapperConfig, RedditScrapperSource

# initialize reddit scrapper source config
source_config = RedditScrapperConfig(
   # Reddit subreddit, search etc rss url. For proper url refer following link -
   # Refer
   lookup_period="1h" # Lookup period from current time, format: `<number><d|h|m>` (day|hour|minute)

# initialize reddit retriever
source = RedditScrapperSource()
Step 4: Configure Analyzer

Note: To run transformers in an offline mode, check transformers offline mode.

Some analyzer support GPU and to utilize pass device parameter. List of possible values of device parameter (default value auto):

  1. auto: GPU (cuda:0) will be used if available otherwise CPU will be used
  2. cpu: CPU will be used
  3. cuda:{id} - GPU will be used with provided CUDA device id

Text Classification

Text classification, classify text into user provided categories.

from obsei.analyzer.classification_analyzer import ClassificationAnalyzerConfig, ZeroShotClassificationAnalyzer

# initialize classification analyzer config
# It can also detect sentiments if "positive" and "negative" labels are added.
   labels=["service", "delay", "performance"],

# initialize classification analyzer
# For supported models refer
text_analyzer = ZeroShotClassificationAnalyzer(
   device = "auto"
Sentiment Analyzer

Sentiment Analyzer, detect the sentiment of the text. Text classification can also perform sentiment analysis but if you don't want to use heavy-duty NLP model then use less resource hungry dictionary based Vader Sentiment detector.

from obsei.analyzer.sentiment_analyzer import VaderSentimentAnalyzer

# Vader does not need any configuration settings

# initialize vader sentiment analyzer
text_analyzer = VaderSentimentAnalyzer()
NER Analyzer

NER (Named-Entity Recognition) Analyzer, extract information and classify named entities mentioned in text into pre-defined categories such as person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc

from obsei.analyzer.ner_analyzer import NERAnalyzer

# NER analyzer does not need configuration settings

# initialize ner analyzer
# For supported models refer
text_analyzer = NERAnalyzer(
   device = "auto"
from obsei.analyzer.translation_analyzer import TranslationAnalyzer

# Translator does not need analyzer config
analyzer_config = None

# initialize translator
# For supported models refer
analyzer = TranslationAnalyzer(
   device = "auto"
PII Anonymizer
from obsei.analyzer.pii_analyzer import PresidioEngineConfig, PresidioModelConfig, \ 
   PresidioPIIAnalyzer, PresidioPIIAnalyzerConfig

# initialize pii analyzer's config
analyzer_config = PresidioPIIAnalyzerConfig(
   # Whether to return only pii analysis or anonymize text
   # Whether to return detail information about anonymization decision

# initialize pii analyzer
analyzer = PresidioPIIAnalyzer(
       # spacy and stanza nlp engines are supported
       # For more info refer 
       # Update desired spacy model and language
       models=[PresidioModelConfig(model_name="en_core_web_lg", lang_code="en")]
Dummy Analyzer

Dummy Analyzer, do nothing it simply used for transforming input (AnalyzerRequest) to output (AnalyzerResponse) also adding user supplied dummy data.

from obsei.analyzer.dummy_analyzer import DummyAnalyzer, DummyAnalyzerConfig

# initialize dummy analyzer's configuration settings
analyzer_config = DummyAnalyzerConfig()

# initialize dummy analyzer
analyzer = DummyAnalyzer()
Step 5: Configure Sink/Informer
from obsei.sink.slack_sink import SlackSink, SlackSinkConfig

# initialize slack sink config
sink_config = SlackSinkConfig(
   # Provide slack bot/app token
   # For more detail refer
   # To get channel id refer

# initialize slack sink
sink = SlackSink()
from obsei.sink.zendesk_sink import ZendeskSink, ZendeskSinkConfig, ZendeskCredInfo

# initialize zendesk sink config
sink_config = ZendeskSinkConfig(
   # For custom domain refer
   # Mainly you can do this by setting the environment variables:
   # ZENPY_FORCE_SCHEME (default to https)
   # when set it will force request on:
   # {scheme}://{netloc}/endpoint
   # provide zendesk domain
   # provide subdomain if you have one
   # Enter zendesk user details

# initialize zendesk sink
sink = ZendeskSink()
from obsei.sink.jira_sink import JiraSink, JiraSinkConfig

# For testing purpose you can start jira server locally
# Refer

# initialize Jira sink config
sink_config = JiraSinkConfig(
   url="http://localhost:2990/jira", # Jira server url
    # Jira username & password for user who have permission to create issue
   # Which type of issue to be created
   # For more information refer
   issue_type={"name": "Task"},
   # Under which project issue to be created
   # For more information refer
   project={"key": "CUS"},

# initialize Jira sink
sink = JiraSink()
from obsei.sink.elasticsearch_sink import ElasticSearchSink, ElasticSearchSinkConfig

# For testing purpose you can start Elasticsearch server locally via docker
# `docker run -d --name elasticsearch -p 9200:9200 -e "discovery.type=single-node" elasticsearch:7.9.2`

# initialize Elasticsearch sink config
sink_config = ElasticSearchSinkConfig(
   # Elasticsearch server hostname
   # Elasticsearch server port
   # Index name, it will create if not exist

# initialize Elasticsearch sink
sink = ElasticSearchSink()
from obsei.sink.http_sink import HttpSink, HttpSinkConfig

# For testing purpose you can create mock http server via postman
# For more details refer

# initialize http sink config (Currently only POST call is supported)
sink_config = HttpSinkConfig(
   # provide http server url
   # Here you can add headers you would like to pass with request
       "Content-type": "application/json"

# To modify or converting the payload, create convertor class
# Refer obsei.sink.dailyget_sink.PayloadConvertor for example

# initialize http sink
sink = HttpSink()

This is useful for testing and dry run checking of pipeline.

from obsei.sink.logger_sink import LoggerSink, LoggerSinkConfig
import logging
import sys

logger = logging.getLogger("Obsei")
logging.basicConfig(stream=sys.stdout, level=logging.INFO)

# initialize logger sink config
sink_config = LoggerSinkConfig(

# initialize logger sink
sink = LoggerSink()
Step 6: Join and create workflow

source will fetch data from selected the source, then feed that to analyzer for processing, whose output we feed into sink to get notified at that sink.

# Uncomment if you want logger
# import logging
# import sys
# logger = logging.getLogger(__name__)
# logging.basicConfig(stream=sys.stdout, level=logging.INFO)

# This will fetch information from configured source ie twitter, app store etc
source_response_list = source.lookup(source_config)

# Uncomment if you want to log source response
# for idx, source_response in enumerate(source_response_list):

# This will execute analyzer (Sentiment, classification etc) on source data with provided analyzer_config
analyzer_response_list = text_analyzer.analyze_input(

# Uncomment if you want to log analyzer response
# for idx, an_response in enumerate(analyzer_response_list):

# Analyzer output added to segmented_data
# Uncomment inorder to log it
# for idx, an_response in enumerate(analyzer_response_list):

# This will send analyzed output to configure sink ie Slack, Zendesk etc
sink_response_list = sink.send_data(analyzer_response_list, sink_config)

# Uncomment if you want to log sink response
# for sink_response in sink_response_list:
#     if sink_response is not None:
Step 7: Execute workflow Copy code snippets from Step 3 to Step 6 into python file for example and execute following command -

Demo UI

We have a minimal UI that can spin up to test Obsei. It's based on streamlit and is very easy to extend for your purposes.


Watch: Obsei UI Demo

Just run

docker run -d --name obesi-ui -p 8501:8501 lalitpagaria/obsei-ui-demo

You can find the UI at http://localhost:8501

Upcoming Release

Upcoming release plan and progress can be tracked at link (Suggestions are welcome).

Discussion Forum

Discussion about Obsei can be done at community forum

Use cases

Obsei use cases are following, but not limited to -

  • Automatic customer issue creation based on sentiment analysis (reduction of MTTD)
  • Proper tagging of ticket based for example login issue, signup issue, delivery issue etc (reduction of MTTR)
  • Checking effectiveness of social media marketing campaign
  • Extraction of deeper insight from feedbacks on various platforms
  • Research purpose


Refer link for attribution.


As project is in very early stage, so we are not accepting any pull requests. First we want to shape the project with community's active suggestion and feedback. If you want a feature or something doesn't work, please create an issue.


Refer releases and projects.

Security Issue

For any security issue please contact us via email

Citing Obsei

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

  author =       {Lalit Pagaria},
  title =        {Obsei - An automation tool for text analysis need},
  howpublished = {Github},
  year =         {2020},
  url =          {}

Stargazers over time

Stargazers over time


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.