Data Extraction and Preprocessing through Webscraping

Webscraping plays a pivotal role in the realm of machine learning, where the quantity and quality of data can directly influence predictive accuracy. Ideally, we would be equipped with comprehensive, well-labelled datasets tailored for our Natural Language Processing (NLP) endeavors.

Yet, in many instances, the desired datasets might be scarce or lacking in depth. A significant portion of this valuable data resides within the intricate web pages of the internet.

Web scraping emerges as a vital tool in this context, enabling us to swiftly harvest pertinent data from platforms like social media, academic journals, or editorial columns. Once extracted, this data becomes a rich resource for sentiment analysis, aggregating diverse opinions about a product or individual, and offering a consolidated view of prevailing sentiments.