Tag Archives: sentiment analysis

The science behind an internet request

Altruism can be found in many shapes on the internet, especially on sites designed for user interaction, like blogs, forums or social networks. The giant Reddit even has a special thread The random acts, on Pizza section which is specialized in giving free pizza to strangers if the story they tell is worth one. It is fun and the motto is as simple as that: “because … who doesn’t like helping out a stranger? The purpose is to have fun, eat pizza and help each other out. Together, we aim to restore faith in humanity, one slice at a time.”

This great opportunity rises an objective popular question in our minds though: What should one say to get free pizza, and furthermore, what should one say to get any kind of free stuff on the internet? A possible answer comes once again from the science of data mining. Researchers at Stanford University analyzed this intriguing problem but limited to Reddit posts.

By mining all the section posts from 2010 until today and passing them through filters like sentiment analysis, politeness and more important if they wore successful or not, a pattern was established.Altruism I

Predictability rate resulted is up to 70 % accuracy and beside the sociological observations, like the positive results of longer posts or the negative results of very polite posts it is interesting to observe the algorithm that made all this possible by dividing the narratives into five types, those that mention: money; a job; being a student; family; and a final group that includes mentions of friends, being drunk, celebrating and so on, which the team  called “craving.”

This study has a very important role in analytics of behavior of peers on the internet and opens a wide area of research for better understanding of online consumers around the world.



Why emotions are important in marketing

I don’t know about you but i haven’t given very much thought of how do i feel the instance that i press the “share” button. I recently found out that i was ignoring a much more important part of online marketing than it seems, and i corrected myself, all because of emotions.

When it comes to what we feel, everything can be expressed as a sum of four basic emotions: happy, sad, afraid and angry, that combine themselves and form a variety of other feelings about which we may or we may not be aware. For better understanding of this we may look at Robert Plutchik’s famous “wheel of emotions” that shows just some of the well known emotional layers.


Studies have also revealed that the emotional state that “gets” the most of the likes is happiness, which is normal if we consider that  our first emotional action in life is to respond to our mother’s smile with a smile of our own. Obviously, joy and happiness are hard-wired into all of us, as discovered by the psychoanalyst Donald Winnicott. And because happiness almost never comes as a self sustainable feeling we can see that the top 10 emotions that people have when sharing something are made of positive ones, as studied by Fractl.

top 10


More than this Jonah Berger, professor of marketing at the University of Pennsylvania’s Wharton School and author of Contagious: Why Things Catch On, conducted a study from which  he found that an article was more likely to become viral the more positive it was.


Of course we shouldn’t neglect that there are also other feelings that may interact with our online behavior.  For instance sadness helps us connect and empathize by producing cortisol, known as the “stress hormone”; and oxytocin, a hormone that promotes connection and empathy. Further research revealed that when we are angry the hypothalamus makes us more stubborn and fear only makes us more desperate to find something or someone to cling on.

Considering all this information is easy to understand the high significance of emotions in marketing, especially when considering that an analysis of the IPA dataBANK, which contains 1,400 case studies of successful advertising campaigns where, campaigns with purely emotional content performed about twice as well (31% vs. 16%) as those with only rational content.

And this is why we can’t underestimate the importance of understanding the science of emotion in marketing!


About sentiment analysis

Hello internet,

As you probably know, we deal everyday with data scraping, which is quite challenging, but, from time to time we tend to ask ourselves what else is there, and especially, can we scrap something else other than data? The answer is yes, we can, and today I am going to talk about how opinion mining can help you.

Opinion mining, better known as Sentiment analysis deals with automatically scan of a text and establishing its nature or purpose. One of the basic tasks is to determine whether the text itself is basically good or bad, like if it relates with the subject that is mentioned in the title. This is not quite easy because of the many forms a message can take.

Also the purposes that sentiment analysis can be to analyze entries and state the feelings it express (happiness, anger, sadness). This can be done by establishing a mark from -10 to +10 to each word generally associated with an emotion. The score of each word is calculated and then the score of the whole text.  Also, for this technique negations must be identified for a correct analysis.

Another research direction is the subjectivity/objectivity identification. This refers to classifying a given text as being either subjective or objective, which is also a difficult job because of many difficulties that may occur (think at a objective newspaper article with a quoted declaration of somebody). The results of the estimation are also depending of people’s definition for subjectivity.

The last and the most refined type of analysis is called feature-based sentiment analysis. This deals with individual opinions of simple users extracted from text and regarding a certain product or subject. By it, one can determine if the user is happy or not.

Open source software tools deploy machine learning, statistics, and natural language processing techniques to automate sentiment analysis on large collections of texts, including web pages, online news, internet discussion groups, online reviews, web blogs, and social media. Knowledge-based systems, instead, make use of publicly available resources to extract the semantic and affective information associated with natural language concepts.

That was all about sentiment analysis that TheWebMiner is considering to implement soon. I hope you enjoyed and you learned something useful and interesting.