Many SEOs believe that the sentiment of a web page can influence whether Google ranks a page. If all the pages ranked in the search engine results pages (SERPs) have a positive sentiment, they believe that your page will not be able to rank if it contains negative sentiments.
The evidence and facts are out there to show where Google’s research has been focusing in terms of sentiment analysis.
I asked Bill Slawski (@bill_slawski) , an expert in Google related patents what he thought about the SEO theory that Google uses sentiment analysis to rank web pages.
“Sentiment is like a flavor, like vanilla or chocolate. It does not reflect the potential information gain that an article might bring.
Information gain can be understood by using NLP processing to extract entities and knowledge about them, and that can lead to a determination of information gain.
Sentiment is a value that doesn’t necessarily reflect how much information an article might bring to a topic.
Positive or negative sentiment is not a reflection of how much knowledge is present and added to a topic.”
Bill affirmed that Google tends to show a range of opinions for review related queries.
“I don’t believe that Google would favor one sentiment over another. That smells of showing potential bias on a topic.
I would expect Google to want some amount of diversity when it comes to sentiment, so if they were considering ranking based upon it, they would not show all negative or positive.”
Bill makes an excellent point about the lack of usefulness if Google search results introduced a sentiment bias.
Some SEOs believe that if all the search results have a positive sentiment, then that’s a reflection of what searchers are looking for. That’s a naive correlation.
There are many known ranking factors such as links that can account for those rankings. There are other factors such as users wanting to see specific sites for specific queries.
Simply isolating one factor and saying, “Aha, all the sites have this so this is why it’s ranking” is naive, it’s cherry picking what you want to see.
For example, the same SEO can look at those search results and see that they all use the same brand of SEO plugin. Does that mean the SEO plugin is the reason those sites rank?
The answer is no.
Similarly, the sentiment expressed in the search results does not necessarily reflect what the searcher is looking for.
This is why I say it is naive to look at one factor such as sentiment and say that’s the reason a site is ranking. Just because you see a correlation does not mean it’s the reason a site is ranking.
Does Google Use Sentiment Analysis for Ranking?
Google’s been largely silent on sentiment analysis since 2018.
In July 2018, someone on Twitter asked:
“…it seems like your search algorithm recognizes and takes into account sentiment. Is there a sentiment search operator?”
Danny Sullivan answered:
“It does not recognize sentiment. So, no operator for that.”
Danny made it clear that Google’s search algorithm does not recognize sentiment.
Earlier that year Danny published an official Google announcement about featured snippets where he mentioned sentiment. But the context of sentiment was that for some queries there may be a diversity of opinions and because of that Google might show two featured snippets, one positive and one negative.
“…people who search for “are reptiles good pets” should get the same featured snippet as “are reptiles bad pets” since they are seeking the same information: how do reptiles rate as pets? However, the featured snippets we serve contradict each other.
A page arguing that reptiles are good pets seems the best match for people who search about them being good. Similarly, a page arguing that reptiles are bad pets seems the best match for people who search about them being bad. We’re exploring solutions to this challenge, including showing multiple responses.”
The point of the above section is that they are exploring showing multiple responses.
Since 2018, Google has stopped showing featured snippets for vague queries like “are reptiles good pets?” and encouraging users to drill down and choose a more specific reptile.
“There are often legitimate diverse perspectives offered by publishers, and we want to provide users visibility and access into those perspectives from multiple sources,” Matthew Gray, the software engineer who leads the featured snippets team, told me.”
Those statements directly contradicts the SEO idea that if the sentiment in the SERPs leans in one direction, that your site needs to lean in the same direction to rank.
Rather, Google is asserting that they want to show diversity in opinions.
Positives and Negatives in Reviews
A Google research paper titled, Structured Models for Fine-to-Coarse Sentiment Analysis (PDF 2007) states that a “question answering system” would require sentiment analysis at a paragraph level.
A system that summarizes reviews would need to understand the positive or negative opinion at the sentence or phrase level.
This is sometimes referred to as opinion mining. The point of this kind of analysis is to understand the opinion.
Here’s how the research paper explains the importance of sentiment analysis:
“The ability to classify sentiment on multiple levels is important since different applications have different needs. For example, a summarization system for product reviews might require polarity classification at the sentence or phrase level; a question answering system would most likely require the sentiment of paragraphs; and a system that determines which articles from an online news source are editorial in nature would require a document level analysis.”
The paper further describes how sentiment analysis is useful:
“parsing and relation extraction (Miller et al., 2000), entity labeling and relation extraction (Roth and Yih, 2004), and part-of-speech tagging and chunking (Sutton et al.,
2004). One interesting work on sentiment analysis is that of Popescu and Etzioni (2005) which attempts to classify the sentiment of phrases with respect to possible product features.”
What stands out about that research is that it is strictly about understanding the sentiment of text.
Yet even though the context is not about ranking because of the sentiment, some SEOs will quote this kind of research and then tack on that it’s being used for ranking. And that’s wrong because the context of this and other research papers are consistently about understanding text, well outside of the context of ranking that text.
Sentiment Analysis Encompasses More than Positive and Negative
Another research paper, What’s Great and What’s Not: Learning to Classify the Scope of Negation for Improved Sentiment Analysis (PDF 2010) presents a way to understand the sentiment of product reviews.
The scope of the research is finding a better way to deal with ambiguity in the way ideas are expressed.
Examples of these kinds of linguistic negation phrases are:
The above examples show how this research paper is focused on understanding what humans mean when they structure their speech in a certain way. This is an example of how sentiment analysis is about more than just positive and negative sentiment.
It’s really about the meaning of words, phrases, paragraphs and documents.
The paper begins by stating the usefulness of sentiment analysis in several scenarios, including question answering:
“The automatic detection of the scope of linguistic negation is a problem encountered in wide variety of document understanding tasks, including but not limited to medical data mining, general fact or relation extraction, question answering, and sentiment analysis.”
How would accurately classifying these kinds of sentences help a search engine in question answering?
A search engine cannot accurately answer a question without understanding the web pages it wants to rank.
It’s not about using that data as ranking factors. It’s about using that data to understand the pages so that they then can then be ranked according to ranking criteria.
One way of looking at sentiment analysis is to think of it as obtaining candidate web pages for ranking. A search engine cannot select a candidate if it cannot understand the web page.
Once a search engine can understand a web page, it can then apply the ranking criteria on the pages that are likely to answer the question.
This is especially important for search queries that are ambiguous because of things like linguistic negation, as described in the research paper above.
If sentiment analysis is used by Google, a web page isn’t ranked because of the sentiment analysis. Sentiment analysis helps a web page be understood so that it can be ranked.
Google can’t rank what it can’t understand. Google can’t answer a question that it can’t understand.
More Sentiment Analysis Research
SUIT: A Supervised User-Item Based Topic Model for Sentiment Analysis (PDF 2014)
This research paper studies how to better understand what users mean when they leave online reviews on websites, forums, microblogs and so on.
This is how it describes the problem being solved:
“…most of existing topic methods only model the sentiment text, but do not consider the user, who expresses the sentiment, and the item, which the sentiment is expressed on. Since different users may use different sentiment expressions for different items, we argue that it is better to incorporate the user and item information into the topic model for sentiment analysis.”
Speech Sentiment Analysis via End-To-End ASR Features (PDF 2020)
ASR means Automatic Speech Recognition. This research paper is about understanding speech, and doing things like giving more weight to non-speech inflections like laughter and breathing.
The research shares examples of using breathing and laughter as weighted elements to help them understand the sentiment in the context of speech sentiment analysis, but not for ranking purposes.
These are the examples:
“1. Yeah, so [LAUGHTER] he’s calling now.
2. Yay, well congratulations, that’s so cool. [BREATHING] I can’t wait.
3. Exactly, [LAUGHTER] I think that’ll go over great, don’t you?
4. That would be wonderful, that would be great seriously. “
The paper describes the context of where it is useful:
“Speech sentiment analysis is an important problem for interactive intelligence systems with broad applications in many industries, e.g., customer service, health-care, and education.
The task is to classify a speech utterance into one of a fixed set of categories, such as positive, negative or neutral.”
This research is very new, from 2020 and while not obviously specific to search, it’s indicative of the kind of research Google is doing and how it is far more sophisticated than what the average reductionist SEO sees as a simple ranking factor.
No Sentiment Analysis Bias at Google
Google has consistently stated that they try not to show pages that reflect a searcher’s sentiment intent (are geckos bad pets?)
In fact, Google says the opposite, that it tries to show a diversity of opinions. Google tries not to be led by a sentiment expressed in the search query.
Example of Google Showing Diversity of Opinion
As you can see in the above screenshot, Google does not allow the negative sentiment expressed in the search query to influence it into showing a web page with a negative sentiment.
This directly contradicts the idea that Google shows search results with a specific sentiment bias if that bias exists in the search query.
You can dig around for Google research and patents about sentiment analysis and you will see that the context is about understanding search queries and web pages.
You will not see research that says the sentiment will be used to rank a page according to its bias.
If the pages that Google is ranking all have the same sentiment, do not assume that that is why those pages are there.
It is clear from Google research papers, statements from Google and from Google search results that Google does not allow the sentiment of the user search query to influence the kind of sites that Google will rank.