kalijauhari / fine-grained-sentiment-analysis

Fine Grained Sentiment Analysis of Phone Reviews on amazon.in

Fine-Grained-Sentiment-Analysis of Smartphone Reviews on amazon.in

This projects includes:

1. Data mining using Selenium, Beautiful Soup, and Requests library
2. Data Exploration using matplotlib, seaborn
3. Modelling using scikit-learn
4. Fine-Grained Sentiment Analysis using TextBlob

Selenium is used to automate the browser and fetching the page source automatically. Selenium Python bindings provides a simple API to write functional/acceptance tests using Selenium WebDriver. Through Selenium Python API you can access all functionalities of Selenium WebDriver in an intuitive way. https://selenium-python.readthedocs.io/installation.html#introduction

Beautiful Soup and Request is used to request the browser opened using selenium and beautful soup is to parse the page source https://www.crummy.com/software/BeautifulSoup/bs4/doc/ Beautiful Soup is a Python library for pulling data out of HTML and XML files. It works with your favorite parser to provide idiomatic ways of navigating, searching, and modifying the parse tree. It commonly saves programmers hours or days of work.

https://requests.readthedocs.io/en/master/ Requests is an elegant and simple HTTP library for Python, built for human beings.

matplotlib and seaborn are the plotting libraries mainly used for exploratory data analysis https://matplotlib.org/ https://seaborn.pydata.org/

https://scikit-learn.org/stable/

TextBlob is a Python (2 and 3) library for processing textual data. It provides a simple API for diving into common natural language processing (NLP) tasks such as part-of-speech tagging, noun phrase extraction, sentiment analysis, classification, translation, and more. https://textblob.readthedocs.io/en/dev/