Search engine rankings look at hundreds of factors associated with websites to determine their rankings. One of the factors that may play a role, given the number of patents and comments about it, is sentiment of texts.

For reference, here are some Google patents or related comments on importance or use of sentiment classification:

Sentiment detection as a ranking signal for reviewable entities (link to Google Patent)

Internal Google System for Large-Scale Sentiment Analysis for News and Blogs

One reason search engines may look at sentiment is to estimate the trust about an entity or brand. We know from the latest Google Quality Raters Guidelines that they treat reputation as an important factor.

In this blog post we will not speculate about the possible mechanisms, but rather just look at the data to see what story it tells us.


Let us first look at the concept of sentiment itself. A sentence can be denoted as positive, if it has a positive meaning, e.g.:

I am happy.

Today was a good day.

This product is great.

Or it can be a negative one:

She was so disappointed.

They were tired.

To carry out the analysis of sentiment on million of sentences, we employed a machine learning model.

Machine learning models, when trained on millions of labelled sentences (usually tweets) are pretty good, better than humans on assessing the sentiment of texts.

We decided to examine two features of ranked pages – their website titles and their text.

Sentiment of a webpage was calculated by determining separately sentiment of each sentence on the webpage and making an average to get the final webpage sentiment. We considered around 200,000 ranked pages with more than 10 million sentences.

Website Texts Sentiment vs. Rankings

The results for sentiment of website texts are as follows:

Statistics R squared is 0.92*. Higher ranked websites seem to have a more positive sentiment. The effect amounts to around 1.0% difference in sentiment between 1. and 10. ranking.

* The results should be evaluated taking into account that the sentiment may be related to many other independent variables that were not included in the model which can introduce bias (so-called omitted variables bias)

Website Titles Sentiment vs. Rankings

We continue the analysis by looking at the website titles sentiment vs. rankings:

Again, the trend is the same – higher ranked pages have more positive sentiment, R2 squared is a bit lower (0.66).

It is quite interesting to find such a relationship and we have thought internally about various possible mechanisms how sentiment can channel through various other important ranking factors.

For example, we may on average less like to read negative content as opposed to positive content. Thus we may stop reading negative content marginally earlier than positive content.

Bu this may then translate to a slightly higher bounce rate for the negative texts as compared with positive texts. If the bounce rate is a negative factor for rankings, this channel could contribute to the observed effect. We came to at least 10 interesting indirect effect theories and the reader may have a lot of them as well.

How to determine the sentiment of your texts

If you want to determine the sentiment of any of your texts or websites, we have a tool available for that which is part of any of our plans. If you are a user with higher demands, we also offer API access to sentiment classifier.

In the next analysis, available exclusively to our paying subscribers, we will examine how the sentiment affects rankings on individual keyword level. And how one can exploit this.