Amazon Product Review Analysis: The Ultimate Guide (2021)
Nearly all Amazon customers check product reviews before purchase. In fact, Trustpilot research indicates that almost 90% of shoppers look at reviews before they buy. Product reviews are often the deciding factor for many customers, so it's important to have a good system for monitoring them.
Why manual Amazon product review analysis doesn’t work
The traditional process of manually analyzing Amazon reviews is extremely time-consuming and inefficient. It also doesn't allow you to see any trends or patterns over time.
With over 4,000 items sold every minute, there are millions upon millions of product reviews on Amazon. As product innovation leads to new products and features constantly being added to the market, review analysis has to dynamically adjust to changing trends.
Ultimately, a lot can change in a day, and millions of reviews are being posted all the time. Manual analysis is not practical or comprehensive—at all.
AI for Amazon product review analysis
For these reasons, AI is needed to analyze and monitor an enormous amount of Amazon reviews as they happen.
Commerce.AI is fully automated and can monitor the entire review history of products. The Commerce.AI data engine has over a trillion data points which it uses to analyze tone, language, keywords, and trends over time to provide valuable insights that increase the success rate of your new and existing products and marketing campaigns.
Indeed, the power of this data engine goes beyond product review analysis, into all facets of communication. Communication today involves streamlining the process of delivering information that’s accurate to both the recipient and sender. There are many use cases for AI in communication technology systems, including:
- Content Generation - Commerce.AI’s data engine is also used to monitor, enhance, and recommend product content
- Customer Experience - Commerce.AI’s data engine empowers customer service teams with access to product use cases, product issues, top features, product comparisons, and topline expert reviews
- Voice Surveys - Commerce.AI is used for the next-generation of customer feedback
Amazon product review analysis is a straightforward way to start with AI in communication analysis, but as you can see, there are many other powerful ways to use AI.
Why is Amazon review analysis important?
The importance of Amazon reviews analysis lies in the significance of reviews in the customer decision-making process before purchase.
Reviews tell you what products and features are trending, what’s in-demand, what’s no longer relevant, how your products and those of your competitors are doing, and more.
It’s no wonder that some sellers have been found willing to pay up to $5 per review with no questions asked. That said, ensuring that you’re analyzing authentic, uncensored reviews is key, as 62 percent of consumers say they will not support brands that engage in review censorship, and consumers are on the lookout for fake reviews.
As a result, a seller can't afford not to have an effective way to monitor authentic customer opinions, enabling them to stay ahead of the competition, and secure their place on top of search rankings.
Properly monitoring these customer opinions also allows sellers to identify trends and patterns over time so they can improve their products.
What makes this process challenging is that there are millions of new product reviews every month. This high volume makes it impossible for sellers to monitor these customer opinions manually, without compromising their profit margins or risking missing out on important trends or patterns over time.
Who writes Amazon reviews?
One way brands can learn about customers' opinions about their products is through reading Amazon reviews.
In general, there are two types of Amazon reviews: verified purchases (those who bought the product on Amazon marketplace) and non-verified purchases (those who made purchases from other sites).
Individuals who leave verified purchase reviews are those who have bought the product directly from Amazon, which also means they liked it enough or were dissatisfied with it enough that they took the time out of their lives to write an Amazon review about it.
In contrast, individuals who write non-verified purchases are often people who have never actually bought the product, but instead have heard about it from friends, or saw an advertisement for it somewhere else online such as Facebook or Google AdWords. Unfortunately, non-verified purchases include entirely fake reviews as well.
What this all means is that sellers should be wary when reading individual Amazon reviews, since there may be unjust bias in the reviews being considered. Instead, when thousands or even millions of reviews are analyzed through AI, these biases balance out and the analysis can provide a more holistic understanding.
Why a massive data engine is needed for review analysis
When analyzing product reviews, you’ll want to consider questions like: How many people left feedback? What was the average rating? What were sentiments like? Which keywords were most common? Were there any common phrases found in feedback? How have review trends changed over time? What are the most liked and disliked features? How are the competitors’ ratings?
Accurately answering these questions through manual analysis is virtually impossible, and even attempts at doing so are extremely time-consuming.
These questions can only be answered through analyzing data at scale, which can be done extremely quickly with AI. In addition, Commerce.AI’s data engine has learned from a trillion historical data points to understand patterns and trends in product reviews.
A guide to sentiment analysis
Sentiment analysis is an attempt to extract sentiment from text, typically by using natural language processing (NLP), computational linguistics, and statistics. The goal of sentiment analysis is to identify and classify opinions about a particular topic, product, or person.
Sentiment analysis can be classified into three types.
1. Predictive sentiment analysis
This type of sentiment analysis aims to predict how people will feel at a future date about a particular topic or product. For example, if we want to know how people will feel about a new phone in six months time, then we can use predictive sentiment analysis.
- This type of sentiment analysis is not used for decision making, but rather for understanding behaviors in the future.
- One of the most common applications for predictive sentiment analysis is to predict a product’s financial performance.
- By using predictive sentiment analysis, product developers can measure the general public's interest in the product and make decisions accordingly (e.g., they can decide on the promotion strategy for the product).
2. Diagnostic sentiment analysis
This type of sentiment analysis aims to understand how people are feeling about an object/topic right now by analyzing historical data on that object/topic. For example, when looking at how people felt about a particular brand over time, diagnostic sentiment analysis would help to determine whether there was an increase or decrease in positive feelings towards the brand over time.
- This type of sentiment analysis is used to investigate problems and identify trends in data that affect decision making.
- While predictive sentiment analysis predicts how people will feel in the future, diagnostic sentiment analysis investigates problems and identifies trends in data that affect decision making.
- A popular example of this application is detecting harmful content online (e.g., detecting fake news).
3. Sentiment classification
This type of sentiment analysis classifies texts with sentiments that are ambiguous or unknown.
Sentiment classification can be useful when trying to find out if a particular piece of text is positive or negative based on its content (e.g., whether a review accurately reflects its star-rating).
NLP techniques used in sentiment analysis
There are several techniques used for sentiment analysis. Let’s explore a few major options.
Bag-of-words sentiment analysis uses the frequency of positive and negative words in an article to determine its overall sentiment. It is an unstructured approach, meaning it does not rely on understanding the meaning of each word.
This approach has been criticized for being simplistic and not able to accurately identify sentiment.
TF-IDF stands for Term Frequency-Inverse Document Frequency. This approach calculates the degree to which a word is present in an article, relative to how common it is in all articles.
TF-IDF sentiment analysis measures the importance of words in driving sentiment in an article and ignores words that are common in all documents. TF-IDF has been criticized for being computationally intensive and unable to accurately measure sentiment.
Word embeddings is a vector representation of natural language data using real values between -1 and +1. This approach calculates the degree to which words are present in an article, relative to how common they are in all articles.
Word2Vec is an artificial neural network model, which maps words from text documents into vectors that are used as inputs for machine learning algorithms like regression or classification models.
In addition to mapping words into vectors, Word2Vec also predicts their probability distribution, making it possible to detect words with similar distributions but different spellings.
A history of sentiment analysis
Sentiment analysis is something that many of us do on a regular basis without even realizing it. From the reviews we leave on websites like Yelp and Amazon to the feedback we give on a product in an online store, it's become an integral part of our culture.
Sentiment analysis has been discussed in research papers since at least 1954. Early on, companies faced a major problem of how to respond to customer feedback which was often contradictory. The introduction of sentiment analysis helped companies to be able to handle this issue by looking at the general tone of the comments, rather than the specifics, and tailor their responses accordingly.
A common process works by first identifying the emotions behind each word - whether that's anger, disgust, fear, joy, sadness, or surprise. This is done through natural language processing, which analyzes the meaning behind each word. This process is then followed by machine learning and natural language understanding which can work out what the text is really about - what caused the emotions or how they are related to each other.
Sentiment analysis has come a long way since it was first introduced decades ago and it's now something we use in our day-to-day conversations. From Siri answering questions in a more humanized way to chatbots being able to understand what we want from them, this field has grown considerably in recent years but there are still challenges that need solving if it's ever going to be perfect.
The future of sentiment analysis
There may come a time in which sentiment analysis is so advanced and omnipresent that it is difficult to escape it.
This combination of the latest advancements in machine learning and Natural Language Processing gives insight into the mood and sentiment driving user engagement with any advertisement, company, or event.
Sentiment analysis was originally thought to be ineffective as humans can identify from context clues that a response is positive or negative if they consciously try – this characteristic is called pragmatic intuition. However, advanced AI techniques and big data have helped sentiment analysis techniques catch up to near-human-level performance.
There will soon be a time when sentiment analysis becomes essential for understanding human interactions online and translating them into something that can aid employers or brands in decision-making processes. More and more of our online data will be leveraged to build human touchpoint strategies empowered by emotional intelligence.
Top sentiment analysis research papers
In recent years, there has been an increased research interest in sentiment analysis. Let’s explore some of the top research papers in this area.
This research paper proposes a system to classify Amazon custom reviews, followed by sentiment analysis, using a rule-based approach.
The paper includes a number of traditional techniques, including POS tagging, feature extraction, opinion word extraction, polarity identification, and more.
A sentiment analysis study was conducted on movie reviews found on Rotten Tomatoes to determine the overall sentiment in each review. The study took into account both the explicit and implicit sentiment within the reviews.
This study applied three different machine learning calculations - Support Vector Machines, Maximum Entropy, and Naive Bayes - for sentiment analysis, achieving higher accuracy than other papers on the well-known movie reviews dataset.
This research paper analyzed online hotel reviews with a supervised machine learning approach, meaning an AI approach that learned from labeled, historical sentiment data.
In particular, two types of information were used: Frequency and TF-IDF, finding that the latter was more effective. This deep-dive is useful for those looking to better understand the technical side of sentiment analysis.
Twitter is a huge source of customer reviews online. As tweets are unstructured, free-form bodies of text, they’re very difficult to analyze combined to other review sources. Analyzing tweets is useful for a wide-range of industries, including healthcare.
This research paper explores various methods for sentiment analysis of tweets, including a meta-analysis of 12 other papers.
This research paper takes a unique approach to sentiment analysis, which starts with creating a dynamic dictionary of words’ polarity based on selected hashtags about a given topic. Then, tweets are classified under several classes, by way of features that fine-tune the polarity degree of a post.
The results were validated by classifying tweets related to the 2016 US election, with good accuracy in detecting positive and negative classes and their subclasses.
This research paper investigates how closely twitter mirrors the real world, by characterizing the relationship between language on twitter and the results of the 2011 NBA Playoff games.
The paper found that tweet language indeed had predictive power.
Top sentiment analysis datasets
Sentiment analysis is an increasingly popular topic in social media because it can be used to identify the tone and attitude of posts, and to thus analyze customer satisfaction, for example.
Several datasets are available that prove useful for sentiment analysis, though some are more high-quality and insightful than others.
One of the most popular sentiment analysis datasets is Stanford Sentiment Treebank, a resource that provides annotated samples of text that are categorized by their sentiment.
This resource includes fine-grained sentiment labels for over 200,000 phrases, and nearly 12,000 sentences. The Stanford Sentiment Treebank helped to push state-of-the-art performance in sentiment label classification.
Another popular resource is Amazon Review Data, which offers sentiment classification for 233 million Amazon product reviews.
This resource offers data from 1996 to 2018 for analysts to analyze product reviews and understand how people feel about the items they are purchasing.
This dataset has collected over 50,000 reviews from over 5,000 restaurants, with a broad distribution in the number of ratings, level of ratings, and cuisines.
This dataset offers insights into how people feel about restaurants by looking at their ratings and reviews.
One of the most well-known sentiment analysis datasets is the Sentiment140 dataset. This dataset is based on a set of 1.6 million tweets extracted using the Twitter API.
This dataset provides for sentiment analysis based on a set of words that were found to be indicative of a positive or negative sentiment. The dataset contains almost exclusively English tweets and has been extensively studied by many researchers in the field of sentiment analysis.
The Sentiment140 dataset has been found to be useful for analyzing customer feedback, as it can measure how much customers like or dislike various brands, for example. In addition, the dataset can be used to generate custom sentiment scores based on certain topics, such as “movies” or “fast food.” However, the Sentiment140 dataset is not without its limitations: the data set does not account for sarcasm or irony, which can lead to faulty conclusions about what customers actually think.
Other sources of product reviews
Many consumers look to Amazon for reviews of products, but what other sources are available? Whether you’re a product person or just an online shopper, you may have found yourself asking this question before. Let’s explore some of the best sources of product reviews that are available.
If you're looking for a review website that brings unbiased commentary, there are many to choose from. Reviews.com is one such example, which includes reviews on everything from home, auto, and life insurance to home security or even broadband.
You can also find reviews on sites like CNET, PC Mag, and PC World. These websites offer in-depth evaluations of each product they make a review of.
Quora is the social network that asks its users questions and then lets them answer those questions with their own knowledge and experiences.
This is a great place for people who are looking for advice on what to buy, which will lead to more in-depth reviews since people feel compelled to share their own knowledge rather than just giving a rating or yes/no answer to a question.
Forums like Reddit are also great places for people to share their thoughts on products they bought and use regularly. This type of forum is often used for sharing opinions on products but can also be used for sharing reviews since these forums typically focus more on discussion than reporting facts.
Another good place to find product reviews is via social media sites like Facebook and Twitter.
While YouTube may not be the most popular site for reviews, it does offer some unique benefits to those who use it as a source of information.
For example, they have video tutorials which can be helpful when comparing two similar products. They also have unboxing videos from people who are reviewing and opening up a new purchase; these videos are often entertaining and show off the item in detail so you get a sense of what it's really like to own it.
How to use product reviews to drive product innovation
Product reviews are a powerful tool for driving innovation. Only by understanding the customer’s needs and wants can a product truly be perfected. Reviews provide an excellent way to get feedback on what’s working and what isn’t.
In today's marketplace, it's not enough to make a product that is merely good enough. Customers are too savvy. They've seen it all, and they know when they're being sold something that’s worse than what’s already out there. That's why it's so important to use customer feedback to drive innovation.
Product reviews serve many purposes, but one of the most important is to point out weaknesses in a product, which can then be refined or eliminated altogether. Without customer feedback, it would be impossible to know that the buttons on your new line of work clothes are too small and difficult to use, or that the cream you're marketing doesn't live up to its claims of being waterproof.
It's also helpful for drawing attention to what customers like about a product and want more of. A lot can be gleaned from this feedback, such as that customers want more variety in colors or that they love the ease with which their new phone slides open. Once those insights are obtained, making changes or adding features can be done without having to guess or assume what customers want.
Further benefits include increased customer loyalty and word-of-mouth recommendations, which can translate into more sales in the long run. Investing time in reviewing products pays dividends in the form of higher customer satisfaction levels and higher sales rates over time.
Product reviews are a powerful tool for driving innovation. A company with a successful product needs only one thing: customers who are willing to provide honest feedback about their experience with said product or service - whether it's good or bad. Product reviews serve many purposes - such as pointing out weaknesses in a product - but one of the most important is for drawing attention to what customers like about a product and want more of.
How to get more Amazon product reviews
Amazon product sales correlate to the number of reviews. This is a clear sign that reviews are an important part of the Amazon ecosystem. Reviews give people confidence in buying a product because they know what others have experienced with the product in question.
The best way to get reviews is by asking for them. It can be as simple as sending out a message to your customers asking if they would be willing to provide feedback about their experience with your product.
You can incentivize this by offering something like a discount of your new product in exchange for their review.
Another way to get reviews is by setting up a feedback form on your website that users can fill out after purchasing one of your products. You should always include an easy mechanism on the page for these users to submit their review, which will then automatically be posted on Amazon once it's been verified as having come from a real customer.
A third way to get reviews is by giving away products for free in exchange for reviews. This is often done in conjunction with setting up a feedback form on your website, but you should be careful not to rely too heavily on this as it can lead to more fake reviews than you otherwise might experience.
Here are four simple tips to follow when asking for a product review.
1. Ask for feedback on the specific product
Don’t just ask for a general review of your company or business. Focus on the specific product or service you want to promote and be clear about what you want from the customer.
2. Offer something in return
Offer customers a free gift or discount in return for their time, and specify what they’ll get if they agree to do this for you. This is a more personal way of asking, and it can increase your chances of receiving positive reviews.
3. Be polite and personable
People are more likely to respond positively if they feel like they’re having a personal conversation than if they feel like they’re being sold to. Keep the email as friendly as possible, and always use “we” instead of “I” when talking about your company or products.
4. Keep it short
This is one of the most important pieces of advice when it comes to writing an effective Amazon review-request email: keep it short! When people receive long emails, they typically tune out before even reading to the end because they assume it’ll be irrelevant information from someone who doesn’t know them personally.
Here’s a dead-simple template that you can use for inspiration:
Subject: Share your opinion and get a discount!
We hope you're enjoying your _____. Your opinion matters to us and we wanted to know what you think about it. Could you take a minute to write a quick review of this product on Amazon?
We’d also like to offer you a discount on your next purchase so you can buy more _____!
Your friends at _____
Amazon product reviews have a significant impact on your business. Amazon takes into account the star rating when determining where to place your product in search results.
In addition, a positive product review can significantly improve your conversion rate. In contrast, customers who read negative reviews are more likely to be cautious about buying your product.
Many companies have a product and want to sell it, but many Amazon products don’t sell. This is because those companies fail to do Amazon product review analysis.
For a new product to succeed, it’s critical to do Amazon product review analysis to make sure there is a market for the product and its specific features, as well as to find merchandising strategies.