Python Sentiment Analysis for Text Analytics
Usually, Sentimental analysis is used to determine the hidden meaning and hidden expressions present in the data format that they are positive, negative or neutral. While text analytics is generally used to analyze unstructured text data to extract associated information with it and try to convert that unstructured text data into some useful meaningful data for business intelligence. Hence, when we apply sentimental analysis for text analytics then, the hidden meaning and expression of text data are taken out in positive, negative or neutral form and later it gets properly converted into meaningful structured text data format.
In sentiment analysis for text analytics process, we not only get meaningful data from row unstructured data but also here we obtain the emotions behind it. By adapting sentimental analysis over text analytics on text database, we can find out what are the trending topics now days and also we can find out its positive and negative impacts on the public. For example, if you are running a restaurant and suddenly there is “spoiled” word gets reflected inside customer’s feedback reviews, through sentimental analysis of this text analytics data; you can directly identify the particular negative emotions of your customer which might affect your sales too. So, in this way, we can get directly emotional analysis of text analytics data that may helpful for us to obtain exact brand situation in the market and what people think about our product and services.
Sometimes, some e-commerce companies have adapted the sentimental analysis of text analytics of their database product reviews and ratings. If in case there are some products which are getting simultaneously negative feedbacks then, such product are identifies through this process and later they are removed from portal or according to negative feedbacks it will be send to modification purpose. Sometimes, some competitors have generated fake reviews on website to reduce product and website branding .Such fake reviews are also identified and manipulated by this methodology.
By using sentiment analysis for text analytics ,we can do competitors analysis for particular product or service like that if for particular product people are preferring competitor website for buying and why not ours? Such analytics can also be obtained from this method. This process have generated real-time reports about product selling and buying which is very helpful for taking cost related decision to higher management people as lot of customers in the market are very cost sensitive.
Static Pages and other sections :
These static pages will be available in project Sentiment Analysis for Text Analytics
- Home Page with good UI
- Home Page will contain an animated slider for images banner
- About us page will be available which will describe about the project
- Contact us page will be available in the project
Technology Used in the project Sentiment Analysis for Text Analytics
We have developed this project using the below technology
- HTML : Page layout has been designed in HTML
- CSS : CSS has been used for all the desigining part
- Python : All the business logic has been implemented in Python
- MySQL : MySQL database has been used as database for the project
- Django : Project has been developed over the Django Framework
- Python Library : We have used numpy, nltk, pyparsing, PySocks python library
Supported Operating System
We can configure this project on following operating system.
- Windows : This project can easily be configured on windows operating system. For running this project on Windows system, you will have to install Python 3, PIP, Django.
- Linux : We can run this project also on all versions of Linux operating system
- Mac : We can also easily configured this project on Mac operating system.