Copying and pasting data or content, which is only available to view in a web browser, eventually becomes a tedious job. This becomes a mammoth task when the data we want to extract or retrieve is extensive. An example could be trying to extract positive statements from thousands of customers, reviewing a product on an e-commerce website.

That is where the concept of web scraping comes into play! The technique of web scraping automates this feature. Hence, instead of copying and pasting data manually, the web scraping software will perform the same task for us within seconds.

Web scraping has an extensive amount of application in various arenas, particularly in the current era where digital marketing is at its peak. A web scraping software or a program will automatically load and extract data from multiple webpages based on your requirement. Once the program runs, you can conveniently save the data extracted to a file in your computer.

Usually, web scraping is done through self-written programs, commonly on Python and/or R language. If we try differentiating these two languages and look at the preference as to which language shall be opted, as the most efficient to code our program in, we come towards a middle ground where each language has its own perks.

Since R is a language centred mainly for statistical computing, R lets it functions do most of the work (Krotov,2018). Python, on the contrary, is more object oriented. Python is extremely dependent on packages and lets you do non-statistical tasks in a straight-forward manner, while R has data analysis built-in and a better statistical support. Both these languages have their own strengths and weaknesses and we can understand that by analysing the fact that the pandas data frames were inspired by R data frames, and the rvest (YouTube, 2019) package was inspired by BeautifulSoup. Ultimately, you may end up wanting to learn both Python and R, so that you can utilise the strength of both the languages, choosing alternatively depending on your needs and what is required by your project.

When we talk about web scraping from the marketing perspective, it can be used to learn more about your customers, competitors and to get a stronghold on the response your business has over social media. This is something which is very sensitive in nature as social media is a like a cloud full of different opinions and ideas, clustered together in one place. There are now tons of web scraping tools available on the Internet like import.io, webhose.io, Scraper, ParseHub etc.

The question arises as to how to bring an ideal outcome? What is the best strategy for web scraping huge amount of your data and presenting an analysis based on it?

And once scrapping is done, there is a need to come up with the best possible analysis on the data we have. For instance, in a situation where there are 457 positive comments on a product and 3 negative — the latter being recent and reporting a fault in the packaging when the product was delivered. After a successful web scraping on this content, we now have all 460 comments as data with us. The analysis we implement on this data will provide us with an overall review about this product since it has been introduced. Our sentiment analysis must be of the level, that each and every comment is put to use and there is an accurate output of our product’s response. This is when methods like polarity detection with RBEM (Rule-Based Emission Model) become crucial. This helps our analysis learn sentiments better, consequently. (Ravi K,2015)

The tool or algorithm we implement, depends on the kind of analysis we require. From entity sentiment analysis, sentiment analysis, lexical analysis, topic detection, and others — we must choose better algorithms and models for implementation. (MonkeyLearn, 2019)

Happy scraping!