How To Speed Up Sentiment Analysis With A Web Scraping Proxy

There is no doubt that big data is a valuable resource for all sorts of businesses today. However, to gain accurate insights from big data, proper structuring and analysis are essential. Sentiment analysis is one of the most important techniques businesses can use to sort through and structure relevant data. By allowing you to sort through the sentiments behind pieces of data like website reviews and social media comments, it helps you understand the overall opinions of your customers.

In this article, we focus on how web scraping and proxies can be used to facilitate the process of sentiment analysis. We start by going through the basics of what sentiment analysis is and what it can be used for. If you are already clear on these, you can use the table of contents to skip ahead and learn how to use proxies to power sentiment analysis.

What Is Sentiment Analysis?

what is sentiment analysis data

Sentiment analysis, a.k.a. opinion mining, is a technique that is used to examine a piece of writing and detect the sentiment behind it. In general, it utilizes techniques such as natural language processing and machine learning to categorize text into positive, negative, or neutral. As such, it is used by companies for purposes such as gauging public opinion, determining brand reputation, and assessing customer response to a particular product or service. There are numerous approaches to sentiment analysis. Here are three of the main ones:

  1. Statistical: This approach relies on statistical or machine learning techniques to interpret data.
  2. Knowledge-based: This approach is less complex than the statistical method and integrates the human element into learning from data.
  3. Hybrid: This method is a combination of statistical and knowledge-based approaches. As it integrates the best of what both approaches have to offer, it often produces the most accurate results.

It is important to note that, while it is extremely useful to businesses today, sentiment analysis is not an exact science. Understanding and analyzing human emotions can be quite tough for computers with subtleties such as humor and sarcasm being particularly difficult to grasp.

What Are the Types of Sentiment Analysis?

web scraping sentiment analysis

There are several sentiment analysis models. They vary in their focus which could be polarity (positive, negative, or neutral), emotions (happy, sad, frustrated, angry), intentions (interested, not interested), etc.

The best model for you would mainly depend on how you would like to interpret data. Here are two of the most important categories:

Coarse-grained

A coarse-grained model utilizes a wide lens to search through whole documents, comments, or sentences. It can be done via subjectivity classification and sentiment detection.

Subjectivity classification, as the name implies,  is used to determine the subjectivity of a piece of writing. It tells you whether the piece of writing is subjective or objective. Subjective texts portray opinions or feelings about a topic. For instance, a comment that says, “I love how loyal Labrador retrievers are. They are my favorite dog breed,” is subjective. On the other hand, a fact-based comment that says, “Labrador retrievers have maintained the top spot on the American Kennel Club registry for 29 years in a row making them one of the most popular dog breeds,” is objective.

Sentiment detection allows you to determine whether a piece of text is emotional or not. And if the text is emotional, it further allows you to analyze whether the emotion is positive, negative, or neutral.

Fine-grained

A fine-grained model looks at pieces of text through more focused lenses. It breaks down sentences into different segments and seeks out the exact meaning of the emotions within those segments.

Various features of products are more easily identified, and are, thereby, easier to correct or replicate. For instance, if a customer leaves a review about a particular product on your E-commerce website, fine-grained analysis will allow you to pinpoint exactly which product they are referring to and the exact part of the product they like or have an issue with.

What is Sentiment Analysis Used For?

how to use a sentiment analysis dataset

We’ve already hinted at some of the general benefits of sentiment analysis. Here are a few ways you can take advantage of these benefits and use this technique to take your business to the next level:

Understanding your customers’ journey

By collecting customer feedback and analyzing the data to determine nuances to the text, you can discover whether or not your customers are happy with your products or services, determine the factors contributing to the positive or negative feelings and adjust to meet your customers’ needs.

Brand monitoring

From websites to social media, blogs,  and forums, brands have a wealth of information available across the internet. Online sentiment analysis allows you to discover not just the volume of mentions you have across various platforms, but the specific and overall quality of those mentions. With vigilant monitoring of online activity, your company is less likely to be blindsided by negative reviews about products and can always stay one step ahead.

Competitor research

While monitoring what customers have to say regarding your company and your products is important, it is equally important to assess the competition and see how customers are responding to them. With sentiment analysis, you can compare how your product reviews measure up against those of your competitors, discover areas in which they are lacking and use the insight gathered to gain the upper hand.

Improving customer care

Customers today expect an instant, personalized, and hassle-free experience with brands. Sentiment analysis can make it easier for you to deliver such an experience to them. For instance, you can categorize incoming support queries according to topic and urgency then direct them to the right department and ensure that the most urgent queries are immediately handled.

How Web Scraping Facilitates Sentiment Analysis

web scraping sentiment analysis

Sentiment analysis is done by inputting relevant data into certain pieces of software. There are several of them out there and you can even build your own from scratch using coding languages like python. Check out this blog post if you would like to find out how to do sentiment analysis in python. But, before you can analyze data, you must first collect it.

Web scraping refers to the automated process of collecting huge amounts of data related to a particular topic. With a web scraper, you don’t need to spend hours or days manually sifting through reviews, comments, and feedback to get a sentiment analysis dataset. You can simply instruct the web scraper to search for the data that you need and organize it in a neat file for further analysis. It can also directly tunnel this data into analytic software.

As such, a web scraper helps make the first step of sentiment analysis much faster, saving you massive amounts of time. The fact that the process is automated also means that there is no risk of human error as would be the case with manual data collection.

Why You Should Use Proxies to Scrape Data for Sentiment Analysis

how to scrape data for sentiment analysis

Because web scrapers make a ton of requests at inhumanly fast speeds, they look suspicious to a lot of websites and are often mistaken for malware. As such, they tend to get banned very quickly. Enter web scraping proxies. We go into detail about what proxies are and how they work in this blog post. But, simply put, a proxy helps you hide your IP address – a distinct code that allows websites to identify and locate a device.

If you use a web scraper to collect data and a website detects it, it is through the IP address that the website can ban the web scraper from gaining access. With a proxy, you can switch up your IP address and continue the web scraping process even after bans. However, certain kinds of proxies are more suited to the process of web scraping than others:

Dedicated, semi-dedicated, and rotating proxies

Based on the terms and conditions of use, proxies can be categorized as dedicated, semi-dedicated, or rotating. Dedicated proxies are those that only a single user has access to. As compared to other types, they are a relatively expensive option. But, because they are used by one person, they can give you faster network speeds and better security.

Semi-dedicated proxies are a more cost-effective option. They are shared by two to five users at the same time. As such, there is a risk of slower network speeds and the possibility of security threats. However, good service providers work to reduce these risks and make sure that semi-dedicated proxies work well for every user.

Rotating proxies have an automatic change in IP address at regular intervals. Since there is a constant change in IP addresses, they offer the highest level of anonymity. For the same reason, they are one of the best proxy types for the process of web scraping.

Datacenter and residential proxies

Depending on where a proxy originates from, it can be classified as either a datacenter or a residential proxy. Residential proxies originate from Internet Service Providers (ISPs) such as AT&T. They are given to homeowners and associated with physical residences. As a result of these features, they look more legitimate and are less likely to be detected as proxies and banned. As such, they are great for web scraping, allowing higher volumes of data to be collected with fewer bans. Their main drawback is their relatively high price.

Datacenter proxies, on the other hand, originate from data centers. They are cheaper and more readily available than residential proxies. They are not associated with ISPs or linked to physical residences. As such, it is easier for sites to detect them as proxies and ban them. In fact, certain sites – e.g. Twitter and Instagram – totally prohibit their use so you cannot gain access to such sites with a datacenter proxy. While you can certainly use datacenter proxies to scrape websites that permit their use, you will have to invest in a huge amount of them to navigate bans and successfully complete your web scraping projects.

To learn more about the differences between datacenter and residential proxies, check out this blog post. It is also important to note that the two classifications stated above are not mutually exclusive. For instance, a proxy can be rotating and residential. In fact, a rotating residential proxy is the most optimal kind for web scraping. Find out more in this post.

Finding the Best Proxy Provider for Online Sentiment Analysis

the best proxy provider for sentiment analysis

While free proxy services exist, it is best to avoid them. Such services can expose your company to data breaches and they are typically too slow and unreliable for web scraping.

If you are looking to reap the benefits of proxies for data collection, it is best to invest in a paid provider. To find the best ones, look out for features such as 24/7 customer support, several IP address locations you can choose from, and a guarantee of fast network speeds.

With 1GBs speed, unlimited bandwidth, and proxies located in 26  countries (and counting!), Blazing SEO offers all of the above and much more. We offer a wide variety of proxies and protocols. So, whether you choose to go with datacenter or residential proxies to enhance your company’s sentimental analysis, we’ve got you. If you have any questions about our services, do not hesitate to get in touch with us.

Wrapping Up

How sentiment analysis works

Sentiment analysis allows you to dig deeper than mere numbers and statistics to uncover the emotions and opinions behind those statistics. It allows you to make sense of data in a way that gives you insights into what your customers are saying and how they feel about you. Using a web scraper, you can quickly collect relevant data and with a proxy, you can navigate bans and further enhance this process.

The information contained within this article, including information posted by official staff, guest-submitted material, message board postings, or other third-party material is presented solely for the purposes of education and furtherance of the knowledge of the reader. All trademarks used in this publication are hereby acknowledged as the property of their respective owners.

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