Using Obsei for scrapping data and analyzing it
Customer feedback is an important aspect to be considered for the growth of the product. If we can analyze what difficulties users are facing, what are the different feedbacks or comments we are getting then we can use those feedbacks to improve our products.
Generally, applications of Google Playstore attract a large number of user feedbacks and comments but it is difficult to read out each one of them and analyze them for improvement in the product. Obsei can not only help you scrapping the feedbacks but you can also perform feedback analysis and support ticket analysis.
Obsei is an open-source python library that has multiple functionalities in terms of scrapping the data from the web, perform sentiment analysis on it, and generate insights.
In this article, we will explore the functionalities that Obsei provides.
Let’s get started…
Installing required libraries
We will start by installing an Obsei using pip. The command given below will do that.
!pip install git+https://github.com/obsei/obsei.git
Importing required libraries
In this step, we will import the required libraries for scrapping the data from the Google play store and performing sentiment analysis on that data.
from obsei.source.playstore_scrapper import PlayStoreScrapperConfig, PlayStoreScrapperSourcefrom obsei.analyzer.classification_analyzer import ClassificationAnalyzerConfig, ZeroShotClassificationAnalyzerfrom obsei.sink.logger_sink import LoggerSink, LoggerSinkConfigimport logging
- Creating Play store scrapper
In this step, we will create the scrapper which will scrape the user feedbacks/comments from the Play store. The only thing we need to download the data is that we need to define the package name of the application we want to scrape. For this article, we will analyze comments for the LinkedIn app on the Play store.
source_config = PlayStoreScrapperConfig(