Sentiment analysis (or opinion mining) is a natural language processing (NLP) technique used to determine whether data is positive, negative or neutral. Sentiment analysis is often performed on textual data to help businesses monitor brand and product sentiment in customer feedback, and understand customer needs.
- Requests: Used for making HTTP requests to retrieve web content from external sources and APIs.
- BeautifulSoup: Used for parsing and extracting data from HTML and XML content on web pages, simplifying data extraction for structured data processing.
- NLTK (Natural Language Toolkit): Used for various natural language processing (NLP) tasks such as tokenization, stop word removal, and sentiment analysis, aiding in text data preparation and analysis. 4.Pandas: Used for efficient data manipulation and analysis, including data cleaning, data aggregation, and working with structured data in tabular formats.
- XlsxWriter: Used to create and format Excel files, including setting cell styles, adding hyperlinks, and enabling custom formatting options.
- Flask: A micro web framework for building web applications and APIs, used to set up endpoints and connect front-end forms to back-end data processing functions.
- JavaScript (AJAX): Used on the front end to asynchronously send requests to the server without reloading the page, enabling a smoother user experience.
You can check out the deployed application at: https://sentilytics.onrender.com