How To Speed Up Sentiment Analysis With A Web Scraping Proxy
As humans, our ability to use language to express our emotions is one of our defining characteristics. We do it every day without giving it much effort or thought. In this digital age, we can even communicate complex emotions with just a few keystrokes. For those instances when we can’t be bothered to type out the words to communicate how we feel, we’ve created icons, aptly named emojis, to express ourselves with just the click of a button.
It’s also generally pretty effortless for us to interpret other people’s expressions of emotion. We don’t have to wonder how someone feels when they leave a review stating the product they just received didn’t live up to their expectations. We know they were probably angry and disappointed. This is a boon for businesses trying to analyze how their customers feel about their products. Their customers don’t hesitate to leave a steady stream of reviews that go into great detail about what they like or don’t about a product or service.
This type of feedback is extremely valuable to any company looking to leverage big data to guide its corporate strategy. As with other types of data, your customers’ emotions, or sentiment, can be mined and extracted so you can analyze it for insights to benefit your business. The difference is that while computers do a great job working with quantitative data such as prices, it’s not so easy for them to analyze emotions. This is where sentiment analysis comes in.
What Is Sentiment Analysis?
Sentiment analysis is the process of using machine learning to examine text and extract meaning related to emotion that can then be categorized and analyzed by businesses to better understand how their customers feel about their products and services. This is done through web scraping bots that use natural language processing (NLP) to categorize customer feedback as either positive, negative, or neutral. By sifting through publicly available comments about a company, sentiment analysis allows brands to understand what aspects of their business are resonating with their customers and where they’re missing the mark.
What Is Sentiment Analysis Used For?
You can use sentiment analysis for an almost endless number of reasons. As with many other web scraping applications, the more you do sentiment analysis, the more uses you’ll find for it. There’s no need to hire a company to perform annoying surveys about how satisfied your customers are with your service when you can simply extract that information directly. To get you started with some ideas, here are some popular uses for sentiment analysis:
Reviews are everywhere, from marketplaces to blogs to forums. Collecting data from your product reviews across the internet lets you get a comprehensive overview of how consumers feel about it. While you probably already know what your bestsellers are, sentiment analysis tells you how customers feel after purchasing.
Brand monitoring is done by analyzing mentions of your brand across the internet. Staying on top of what people are saying about your company allows you to figure out where you’re crushing it and where you could improve. It also gives you the opportunity to course-correct before you go too far in the wrong direction. If you see customer sentiment is trending negatively towards your most recent ad campaign, you can quickly change directions before it spreads too far.
You can also use brand monitoring to analyze how your brand is perceived in different geographical locations. Your message may be resonating with a market in large cities but missing the mark with people in small towns. Analyzing customer sentiment lets you dig into the details instead of just getting the overall picture. With this type of data, you can hone in on areas you can impact the most.
Sentiment analysis is useful in all phases of marketing. Before you launch a marketing campaign, you can determine how people feel about the product you’re advertising. Determining what features they’ve loved or hated on similar products can help your marketing team devise a campaign that emphasizes the features consumers want most.
Once you’ve launched your campaign, you can monitor the sentiment surrounding it. If it’s performing well in some areas but not others, you can analyze sentiment to uncover the problem. If it’s falling flat, you can find out why and make adjustments. Keeping an eye on your ad performance and comparing it to your previous campaigns will let you know if your performance is better or worse and what results you can expect.
With the advent of targeted marketing, the days of launching one big ad campaign that everyone watches gathered around the television are mostly over. Using sentiment analysis, you can change up your ad campaigns based on who you’re marketing to. You may discover one campaign performs well in one geographic area, but you need to use a different language to resonate with people elsewhere.
More customers are reaching out to brands on social media with complaints and concerns, expecting a quick response. While this puts increased pressure on your customer service department, it also gives you a chance to shine. You’re no longer speaking to one irate customer at the back of your store; you’re talking to thousands or more potential customers in a public forum. If you quickly address a complaint, you can turn an unsatisfied customer into a major fan. By data scraping customer sentiment, you can quickly route customer service issues to the appropriate person to be handled effectively.
Sentiment analysis isn’t just for heading off negative customer service episodes, however. You can also identify happy customers who may be more receptive to upselling.
When designing a new product, understanding how consumers feel about existing products will give you insight into what they want. Mining customer sentiment for product ideas and features can be a treasure trove of ideas for your product design team. You’re not just limited to your products, either. If your competitors’ product has been outperforming yours, you can find out why and figure out how to improve.
You can identify your customer’s pain points, including the language they use to discuss them. This will help you find out what they want and how they talk about what they want. When you’re planning your marketing campaign, you can reflect their language.
Once your product has launched, you can analyze customer sentiment based on specific features. In addition to showing you what you need to improve, this can give you direction for future products.
Customer sentiment can be invaluable in all types of market research. If you’re looking to expand into an area, you can determine if your business will likely succeed or if the market is already saturated. Identifying early trends will give you an advantage over your competition.
You’ll never need to rely on stale, generic data from marketing firms to discover what your consumers want. Sentiment analysis lets you hyper-focus on your ideal customer — what they want, how they feel, and how you can reach out to them.
Monitor your competition
You’re not limited just to monitoring your customers. If your competitor launches a new product, you can keep an eye on how well it’s received. Analyzing their customer sentiment will point out any weak spots in their business you can use in your marketing strategies. You’ll be able to see what’s working for them. Maybe they’re marketing to a group you haven’t thought to target. You can also learn from their mistakes. If their product gets a lot of negative reviews for a specific feature, you can be sure to improve your own product and center your advertising around that.
How Sentiment Analysis Works
Sentiment analysis works by using NLP and machine learning algorithms to detect the underlying emotions in publicly available data such as reviews, articles, and social media posts. This can be accomplished in several different ways. The best option for you will depend on how much data you have to analyze and the level of detail you want to extract. There are three basic methods of sentiment analysis:
This is also called the automatic method. It uses machine learning techniques to interpret text. The sentiment analysis task feeds text to a classifier that returns a category, such as positive, negative, or neutral.
The process starts with training and predicting. The model learns to associate text with a tag based on test samples. The text is transformed via a feature extractor into a feature vector. The machine learning algorithm generates a model based on the pairs of feature tags and vectors fed into it.
The text is most commonly transferred through a “bag-of-words” (BOW). This method uses a vocabulary of known words and a measure of how many known words are present. It’s called a bag of words because the structure and order of the words are not considered.
Another method of feature extraction is based on word vectors. This method attaches similar representations to words with similar meanings, which can improve the classifier’s performance.
Once the text is transformed, it’s classified based on a statistical model. Some of the most commonly used models are:
- Naïve Bayes, a type of probabilistic algorithm based on Bayes’s Theorem
- Linear regression, an algorithm used to predict a value based on a set of features
- Support vector machines, a non-probabilistic model that works well with a limited amount of data
- Deep learning, a wide range of algorithms that uses artificial neural networks to attempt an imitation of human thought
This rules-based method relies on human-made rules to categorize words. The exact method will vary based on the rules, but it usually starts with defining two lists of words, often one positive and one negative. Then it will count the number of each that appears in the text it’s analyzing. Suppose the system detects more positive than negative words. In that case, the sentiment for that text is listed as positive, or vice versa.
This is often a simpler process and easier to implement, but it requires a lot of maintenance and fine-tuning. Your results will not be as nuanced as with a statistical model. It will also generally require more tweaking and updating. While it starts out simple, adding new rules in response to new vocabulary can lead to a very complex system.
This method is a combination of knowledge-based and statistical methods. Because it combines the best of both methods, it’s often the most accurate. Sentiment analysis is a challenging process in machine learning. Human language is so nuanced that it can be very difficult for a computer program to analyze it accurately.
Sentiment Analysis Models
Two of the main sentiment analysis models are coarse-grained and fine-grained:
Coarse-grained analysis takes a broad view. It analyzes the entire text as a whole, whether it’s a complete document, review, comment, or sentence. It does this using subjectivity classification and sentiment detection. The purpose of this is to determine if there’s value in further analysis.
Subjectivity classification first defines the text as subjective or objective. As you might find in a rave review, subjective texts express an opinion and usually an emotion, too. Objective texts are more fact-based and less likely to convey an emotion. They won’t provide much data as far as sentiment analysis, while subjective texts will.
Unlike coarse-grained models, fine-grained models analyze the grammatical structure of the text. Fine-grained models usually have expanded polarity lists, not just positive and negative. They’re better at identifying particular features and determining sentiment related to them. This model will give you more detailed information rather than just a general overview. Some features of fine-grained models include:
Detailed polarity. Instead of just positive or negative, you can define a range of lists from very positive to very negative.
Emotional tone. Many systems aiming to detect emotional tone use lexicons — a list of words and their related emotions. For instance, a lexicon for anger might include words like “furious,” “hate,” “kill,” etc. The problem with detecting emotional tone with lexicons is that the same word can express different emotions depending on context.
Aspect-based analysis. When you’re interested in finding out what particular features customers liked or disliked about a product, aspect-based analysis can help. You can define an aspect-based classifier and determine if the sentiment related to that feature is positive or negative.
Benefits of Online Sentiment Analysis
As much as 90 percent of the world’s data is unstructured. Social media posts, blogs, articles, and online forums all provide massive amounts of data with a treasure trove of information. However, without an efficient method for sorting and analyzing this information, it can’t be accessed or used. Sure, you could hire an intern to spend all day searching through social media posts relating to your brand and inputting them into a spreadsheet. But that would obviously be inefficient and resource-intensive.
Sentiment analysis offers the following benefits:
Large-scale data analysis
Going back to your hypothetical intern, even if you had a massive team of them, there’s no way they could sort through the available data. There’s simply far too much of it. However, when you run data for sentiment analysis, you can quickly process and extract huge amounts of data.
While classifying emotional tone can be difficult for computers, machine learning ensures that it will be done consistently. If your team was doing it manually, there’s a good chance that a word one person classifies as anger, another would classify as frustration. A consistent standard lets you continually improve your accuracy. It also reduces the dependence on subjective standards. Your results may not always be correct, but they’ll be consistent, allowing you to develop models for improvement.
If you have a crisis developing on social media today, learning about it next week isn’t going to help you deal with it. A web scraper can continuously perform sentiment analysis, allowing you to put out small fires before they rage out of control. The up-to-the-minute results let you respond quickly to any customer service issues as they arise, not days later when your customer is fuming. Customer sentiment in real-time also enables you to interact with customers who are ready to buy or receptive to upselling.
Challenges With Sentiment Analysis
The success of sentiment analysis is based on how closely it aligns with human judgment. However, humans only agree 80 percent of the time, so analysis can’t achieve 100 percent accuracy. If it did, humans would still disagree with about 20 percent of the results. Sentiment analysis has a way to go before it’s fully reliable. Machine learning has made a lot of advances, but there are still significant challenges involved, including:
Sarcasm, irony, and intent
When people express their feelings using words that mean the opposite of what they’re trying to say, it can be almost impossible for a web scraper to determine the actual intent. It can even be difficult for humans to detect sarcasm, as evidenced by frequent references to the need for a sarcasm font on message boards.
Lack of context
The same remarks could be considered positive or negative depending on the context. A lot of unstructured data doesn’t exist in a neatly formatted context. In a message board, for instance, many replies are divorced from the original comment.
When people make comparisons, your scraper may have trouble determining if a comment is positive or negative. Some comparisons are clearly positive, such as “This product is so much better than the other guy’s product.” However, in comparisons that don’t have clear context clues, such as, “This is better than nothing,” classifying it as positive or negative can be difficult.
How to Do Sentiment Analysis in Python
Python’s NLTK is a great option for performing simple sentiment analysis. VADER’s SentimentIntensityAnalyzer is probably the easiest method if you’re just getting started. It comes with a preloaded lexicon. Of course, as you go deeper into your analysis, you’ll want to build a personal lexicon library that’s specific to the data you’re analyzing. After downloading, you can simply import your dataset and incorporate it into the dataframe to read the sentiment analysis.
The quality of your results will also depend largely on your dataset. Since the whole point of sentiment analysis is to get as much information from as many relevant sources as possible, you’ll need an automated way to extract and export data. Web scraping is by far the most efficient way to produce relevant datasets.
Sentiment Analysis in R
The Sentimentr package is a quick and easy way to get started if you’re working in R. One great feature about Sentimentr is that it corrects for inversions, so if a review reads, “This is not good,” it will recognize that as a negative sentiment, instead of being misled by the word “good.” You just need to install and load the package and then load your dataset. Once the program runs, it will generate different variables that were analyzed. The “ave_sentiment” variable expresses the average sentiment score in a number.
Web Scraping for Sentiment Analysis
Web scraping is the process of using a bot to extract publicly available data from websites and export it into a usable format for analysis. Web scraping is a natural fit for sentiment analysis. Web scrapers don’t evaluate data; they simply extract it and export it to a readable format. You’re going to need to gather data before you can analyze it. You can tell your web scraper to search for all relevant mentions of a particular product. This data can then be fed to your sentiment analysis program for evaluation.
As with sentiment analysis, you may run into challenges when you’re scraping data. Web scrapers can extract data far faster than a human can. This, of course, is the biggest benefit of using one. However, this is also the reason you have problems using your web scraper. Many websites have anti-bot security measures in place. These measures automatically block an IP address that displays bot behavior, such as sending repeated requests in a short amount of time. If your IP address is blocked, your scraping project will be as well.
Using Proxies for Web Scraping
One way to avoid bans when you’re scraping is by using proxies. A proxy acts as an intermediary between your computer and the website you’re visiting. When you use a proxy, your computer sends its request to the proxy server first. The proxy server then assigns you a different IP address and sends your request to the website. The website then sends a response to the proxy server, which sends it back to you.
However, simply trading your IP address for another will result in your proxy IP address getting banned, which won’t help you much. The solution is to use a pool of rotating proxies. Using rotating proxies when web scraping will help prevent bans by making your bot behavior seem more human-like. It assigns a different IP address for every request. This will make the website think every request is coming from a different, legitimate user.
Web Scraping Best Practices
Proxies aren’t a free pass for unethical behavior. Even though you’re not likely to get banned using rotating residential proxies, you should still be a “good neighbor” and avoid practices that can harm the websites you’re scraping. These include:
Check the API first
Before you scrape a website, check if the data you need is available in their API. If it is, use that. It’s faster for you and easier on the website.
Abide by the robots.txt file
This will let you know the website’s preferences for scraping, including information such as how far apart to space your requests.
Imitate human behavior
Just because you can send thousands of requests per second doesn’t mean you should. Even if a website doesn’t have a robots.txt file, space out your requests, randomize the intervals, and give the server time to respond. In addition to not overloading the server, this type of activity is less likely to attract the attention of anti-bot security measures that could get you banned.
Types of Proxies
Proxies are a complicated subject. There are many types, many ways to categorize them, and different advantages and disadvantages. The best type for you will depend on your circumstances. We’ll discuss several types so you can make an informed decision.
These are available to anyone for free. They’re a nightmare in terms of performance, security, and reliability. There are almost no use cases where these are a viable option and certainly not for web scraping, which demands a high degree of performance, authority, and reliability.
Shared proxies are just that, shared by many users. Public proxies are an obvious example of shared proxies, but you can also buy private shared proxies. These have all the same issues as public proxies and are not a good option for web scraping.
These proxies are shared by fewer users, usually around three. These are much less likely to experience lagging as a result of too many users. You may still have some issues with a “bad neighbor” effect, which is getting banned because of bad behavior on the part of someone who shares the same proxy IP addresses as you. However, this is less likely to be a problem if you buy from an ethical proxy provider that vets its users.
Private proxies are reserved solely for your use. They’re the best option in terms of performance and reliability. You don’t have to worry about other users or being slowed down by them. These proxies are more expensive than semi-dedicated or shared proxies, but they’re the best option for enterprise web scraping.
Data center proxies
Data center proxies originate from and are stored in data centers. Unlike residential proxies, which we’ll discuss below, data center proxies aren’t issued by an internet service provider (ISP). They aren’t associated with a physical address, so it’s easy for websites to determine they’re not regular users. This makes blocking more likely. In fact, some sites block all traffic from data center IP addresses. Other websites aren’t so drastic but will ban the entire subnet associated with a suspicious data center IP address.
Data center proxies are the most common type. They are plentiful and cheap, which is good because you’ll need a lot of them when web scraping. Data center proxies are also fast, especially if you use one that’s close to the server you’re targeting. They can be a good option if you’re scraping websites that don’t block data center IP addresses and if your budget is limited.
Blazing SEO offers data center proxies located in 29 countries. We have over 300,000 IP addresses, so you’ll never have to worry about having enough. We also have over 20,000 unique C-class subnets, so you have plenty of options for getting back up quickly if you’re banned. We offer free replacements, along with unlimited bandwidth and connections. Our 25-petabyte capability means we have the resources to provide you with more uptime and help you achieve success with your scraping projects.
Unlike data center proxies, residential proxies originate from an internet service provider (ISP) and are associated with a physical address. These proxy IP addresses are exactly like the IP address at your house. They look and perform exactly like normal IP addresses because that’s what they are. They’re sourced from actual users so that they won’t be banned solely based on their origins.
Residential proxies are the best option for large-scale web scraping projects such as sentiment analysis. They are more reliable and have more authority than data center proxies. The disadvantage of residential proxies is that they’re more expensive than data center proxies. However, residential proxies will pay for themselves in the increased quality, security, and reliability they offer. There’s no better option for web scraping.
At Blazing SEO, we provide the most reliable rotating residential proxies you can buy. These proxies offer the most ban protection and highest authority. They’re the perfect solution when you want a high success rate without having to worry about how many requests you’re sending per minute. A rotating proxy has a new IP address for each connection, which lessens the chance of getting banned.
Choosing a Proxy Provider
There are a lot of companies who will sell you proxies, and some who will even give them to you for free. So why does it matter who provides your proxies?
When you use cheap (or worse, free), proxies, you open yourself up to security risks. You usually don’t have any control over who else is using the same proxy IP addresses you are. Additionally, almost all free proxies use HTTP, which doesn’t encode your data. High-quality proxy providers vet their users and control who has access to their proxies, giving you an additional layer of security.
When you use public or shared private proxies, there are usually so many other people on them that their performance is very sluggish. Overloaded servers mean that no matter how fast your web scraper may be, it will slow to a crawl because of the lag. You may also experience a lot of downtime because of bans. Even if you’re rotating proxies, you may get banned often. If other people use the same IP addresses as you and get banned, you will as well.
Cheap and free proxy providers who offer residential proxies are often sourcing them unethically. Many residential proxies are stolen or obtained with “permission” hidden in the tiny print of Terms of Service pages. You should ask your proxy provider how they source their proxies. If they aren’t forthcoming and transparent about their practices, you can be almost certain they aren’t above board.
Proxies have long been associated with black hat or gray hat practices, and there are still plenty of malicious actors who use proxies for unethical purposes. If your proxy provider doesn’t ask you why you’re using their proxies, you can bet they don’t ask anyone else either. Without vetting their clients, they have no way of knowing what they’re doing with their proxies.
If you go with cheap or free proxies, you won’t have the customer support you get with more reliable providers. If you run into any issues while you’re scraping data, you’ll have to solve them on your own.
Blazing SEO Proxies
We know the standards you should use to choose a proxy provider because we set them. We are bringing proxies out of the shadows and into the light. We are completely transparent about our sourcing practices. Our residential proxy end-users give fully informed consent, are compensated, have control over how their proxies are used, and can opt-out at any time.
We also vet our clients. We don’t give anyone access to our proxies until we know why they intend to use them, and we follow up on any signs of unethical use. We know your reputation is as important to you as ours is to us, so we want you to know when you do business with Blazing SEO, you’re working with the most ethical company in the industry.
You’re never alone when you partner with Blazing SEO. We offer 24/7 customer support, 365 days a year, for any issues you run into using our proxies. We have a team of dedicated experts that are standing by and ready to help. From small companies to government entities to Fortune 500 companies, we customize solutions for organizations of all sizes.
If you’d like to get straight to sentiment analysis rather than spending your time and resources building a scraper and managing proxies, Scraping Robot can do it all for you. Scraping Robot provides structured JSON output, so your files are ready to be used in your sentiment analysis program. You don’t have to worry about blocks, CAPTCHAs, or managing your proxies. Our system was built with developers in mind. We provide documentation that allows you to get up and running in no time. We update our modules regularly, but if you can’t find one that suits your needs, we’ll build a solution just for you. We focus on the details so you can focus on getting the valuable metadata you need.
Sentiment analysis is the best tool you have to truly understand your customers. People discuss their feelings and experiences freely on social media and other sites on the internet. Their comments are pure gold for companies who know how to mine and analyze them. The ways you can use sentiment analysis to benefit your business are unlimited. You can find the answer to almost anything you want to know about your customers, and you can get the answer in their own words directly from them. That’s far more valuable than the latest trend report using data from customers that aren’t necessarily even your target audience.
If you’re ready to get started using sentiment analysis to help drive your business decisions, reach out to our team. Nobody is willing to go further to design solutions for your success than Blazing SEO. We want to partner with you and help you achieve your goals.
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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