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Python Sentiment Analysis for Text Analytics
Sentiment analysis is typically employed to ascertain whether the data format's hidden expressions and hidden meanings are neutral, positive, or negative. While unstructured text data is typically analyzed using text analytics to extract related information and attempt to transform the unstructured text data into some meaningfully relevant data for business intelligence. Therefore, the underlying meaning and expression of text data are removed in positive, negative, or neutral form when we perform \strong>sentimental analysis for text analytics, and it is then appropriately translated into meaningful structured text data format.
When conducting a sentiment analysis Row unstructured data provides us with valuable information for text analytics processes, but it also reveals the emotions that underlie the data. By modifying the approach known as "sentimental analysis," Text analytics is over We can discover the current hot themes on text databases, together with their advantages and disadvantages for the general audience. For instance, if you are a restaurant owner and a customer's feedback review contains the word "spoiled," you can immediately identify the specific negative emotions of your customers through sentimental analysis of this text analytics data, which may also have an impact on your sales. Thus, we can obtain immediately from text analytics data an emotional analysis that may be useful to us.
We may undertake competitor analysis for a specific product or service by employing sentiment analysis for text analytics. For example, we can find out why consumers choose to purchase a particular product from a competitor's website rather than ours. This approach can also yield such analytics. Since many consumers in the market are extremely cost sensitive, this procedure has produced real-time reports concerning product sales and purchases, which are particularly helpful for higher management when making decisions regarding costs.
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
- JavaScript : All the validation task and animations has been developed by JavaScript
- 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.
