Last Updated on August 10, 2020
For a long time, businesses and bloggers have used reviews as a way to improve their website rankings, giving little regards for relevancy and users’ benefit.
It is a bad habit and you need not practice it, but we are not here for a moral lesson but to discuss how the negative reviews affect website ranking in Google. In a recent Google Webmaster Hangout, John Mueller answered whether negative reviews are harmful to a website or not.
While you can go through the entire Hangouts session here, we have shared details underlying the discussion in this post.
During the session the question regarding the topic that was asked is:
“So, about negative reviews not hurting, so if you have a “bad” reputation online and you see a lot of negative stuff about your company.
…Would that hurt, potentially, your Google ranking for keywords?
…Could Google look at that and say, Oh this is a bad company, we’re not going to rank it as well because they have a lot of negative reviews.
You said I don’t think that would hurt overall rankings for a website if there’s a bad reputation around the site.”
What the whole discussion on negative reviews and rankings is all about?
The question was asked on the backdrop of a 2010 incident that was reported in the NYTimes where an online merchant was allegedly ranking higher because of the large number of links pointing out to their sites from angry customers.
This very article resulted in a response from Google that announced the introduction of sentiment analysis, based on the “Being Bad to Your Customers is Bad for Business” approach and turned negative comments into negative votes.
In a blog post in 2010 Google clearly stated that it will be considering negative comments as negative votes that will negatively affect website ranking.
What Mueller said about Negative Reviews and Rankings
There was a straightforward answer from Google’s John Mueller. He affirmed that if the online signals for a website are predominantly negative then it can have an adverse effect.
He also noted that negative reviews are a normal thing for business and in small numbers, it should not have any impact on ranking.
This is what Mueller said:
“…That’s something where if all of the signals point in that direction, I could imagine that we might pick that up.
But if you’re talking about… there are a handful of people that are upset and they’re writing these random things online, and there are lots of people that are happy with your site, and everything is normal, then that’s not something where I would really worry about.”
It can be taken from these statements that John is trying to clarify that just random negative reviews shouldn’t be bothering or must be considered as negative ranking factors.
You have to keep in mind that is perfectly normal for any business to have a few negative reviews. What is not normal are predominantly negative signals.
Mueller continued:
“I think those situations where it’s like there are a lot of people that are really upset about your site, those are probably pretty rare. Not something that most normal sites would run into. “
Is Google still using the 2010 Sentiment Analysis Algorithm?
One of the participants asked Mueller if the 2010 Sentiment Analysis algorithm is still being used in the Google SERPs.
To which he answered that implied that the algorithm from ten years ago might not exist in the same form as technology is constantly changing. He analogized it same with the phones we use today and how different they are from what they used to be 10 years ago.
Here is what Mueller answered:
“I don’t know if that specific thing from 2010 is still around. Because things change quite a bit over time.”
He also added:
“…things have changed quite a bit in ten years. So that specific thing is almost certainly not there in the same way as it was back then.
…we probably take something similar into account.”
He again affirmed that for an impact there have to be the predominant amount of negative signals that indicate some form of an outlier type event.
“But that is something that we would try to pick up on. If it’s something where we see that everything is really bad about this site, that might be something that our algorithms try to pick up on.
But that’s something where in general, these kind of things because they’re so vague sometimes, really need to be really strong.
It really needs to be a strong signal for us to say, Okay, we really can trust… this problematic information and apply it appropriately for the site’s ranking.”
Mueller reaffirmed participants that a few bad reviews are the norm and won’t have any effect negative or otherwise on a site.
“…a case of a normal website and there are a bunch of bad reviews out there but they’re kind of embedded in the web in a normal way. And that feels like something where it’s easy to get obsessed about a handful of bad reviews.
And it’s probably not something that would drastically affect the outcome in search.”
John Mueller’s concluding thought on the topic seemed to be pointing out the fact that algorithms do not apply to every situation.
He said:
“I think that’s always tricky with these kind of things, to… infer from in a small situation where you are in, this applies and then to take that and say, well that applies to everything.”
Extracting Sentiment From Reviews: The Challenge
It is not that easy to extract sentiment from online reviews as there is a lot of noise included. For better understanding think of about these factors where were listed in a Google research paper that can introduce noise into sentiment analysis.
1. Reviewers leaving the comments can be highly biased. Some have an agenda while others are working for the competitors. While others can be acting out of a rumor.
2. Not all sites have equal reviews. On some sites, people might have left reviews without even purchasing the product. There are some sites where professional leave reviews after professional-grade testing.
3. How are can reviews graded for sentiment can be dealt with equally? Three stars from the reviewer might not be the same from another reviewer.
4. How does an algorithm define an untrustworthy review? What signals can be an indication of an untrustworthy review?
5. Studies suggest that already existing reviews can influence the subsequent reviews, creating a polarization ultimately.
These points are enough to understand why John Mueller and Google believe that random negative reviews can’t badly affect ranking. A lot of noise in these reviews is the reason.
