Text analysis is a machine learning technique that allows companies to automatically understand text data, such as tweets, emails, support tickets, product reviews, and survey responses. Text analysis to extract specific information], like keywords, names, or company information from thousands of emails, or categorize survey responses by sentiment and topic. Whereas, Text analytics is the automated process of translating large volumes of unstructured text into quantitative data to uncover insights, trends, and patterns through statistical pattern learning. To perform text analytics you'll need to analyze text and then use data visualization tools to showcase your results. Here, 151 Reports from the site is taken and analyzed according to the word count, Positive Score,Negative Score, Polarity, Uncertainty, Constraining, Positive Word Proportion, Neegative Word Proportion, Uncertainty Word Proportion and many more. Initially, data is cleaned and later function file is formed which is further called in main file. At the end, dataframe is formed which involves the total of all the data points.

Pandas, Numpy, Matplotlib, bs4, requests, BeautifulSoup, webbrowser, os, nltk, urllib.request, re, Pyphen

Python

Jupyter Notebook