{"id":13264,"date":"2021-05-25T13:55:01","date_gmt":"2021-05-25T17:55:01","guid":{"rendered":"https:\/\/vernai.com\/?p=13264"},"modified":"2021-05-25T13:55:01","modified_gmt":"2021-05-25T17:55:01","slug":"what-to-expect-from-your-emotion-recognition-software-part-2","status":"publish","type":"post","link":"https:\/\/host.tucknologies.com\/~v3rn4dm1n\/2021\/05\/25\/what-to-expect-from-your-emotion-recognition-software-part-2\/","title":{"rendered":"What to expect from your emotion recognition software Part 2"},"content":{"rendered":"

In part one of our series,<\/a> we compared sentiment analysis to emotion recognition in order to show the differences between the two approaches. We\u2019re back to do another comparison and talk a little about the methodologies and how they differ.<\/p>\n

First, a quick refresher: Sentiment analysis categorizes and analyzes text to determine if the writer\u2019s attitude towards a particular person, place or thing is either \u201cpositive,\u201d \u201cnegative,\u201d \u201cneutral,\u201d or \u201cmixed.\u201d Emotion recognition (or emotion detection) is the process of identifying and attributing emotional states based on communication, such as Anger or Sadness.<\/p>\n

Sentiment analysis measures polarity: Good\/Bad. Emotion recognition measures emotions.<\/p>\n

 <\/p>\n

Is emotion recognition always better?<\/h1>\n

 <\/p>\n

We\u2019d love to say that it is, but, sadly that\u2019s not the case.<\/p>\n

\"\"Emotion recognition isn\u2019t always applied ethically<\/a>. Facial recognition software, for example, has been shown to be problematic with bias and has led to a retraction of many projects and products. This kind of emotion recognition is easily faked, and some of the fundamental scientific assumptions are problematic at best.<\/p>\n

Detecting emotions from aural tonal analysis seems promising, but it too is problematic. Emotional Prosody<\/a> only works in the normative ranges, has considerable problems with cultural nuances<\/a>, and in the end is only analyzing modulation. Let\u2019s face it: You can\u2019t do gush to your friends about your new object of affection using grunts and groans. (Though you would look pretty silly trying).<\/p>\n

Unfortunately, some operators are relying on sentiment analysis at the core of their \u201cemotion\u201d recognition and these hybrid models only exacerbate the problem. As we\u2019ve discussed in our other series (Part 1<\/a>, and Part 2<\/a>), science has not agreed on a consensus model. Some that claim to have found multiples of emotions are likely measuring the same variables, confounding their results. Or, they\u2019ve created a ML model from a specific data set that is likely to prove biased.<\/p>\n

In these cases, emotion recognition isn\u2019t better. It\u2019s often confusing to an operator. When psychology can\u2019t agree–some models have 5<\/a>, 7, 27<\/a>, 13 or so<\/a>; or up to 40 different \u201cdistinct\u201d emotions\u2014you are not measuring the emotions that are present. You have a mess.<\/p>\n

Some models are clearer<\/h1>\n

 <\/p>\n

\"\"VERN is based on neuroscientific conceptualizations of the mind and does not rely on psychological studies that may or may not be replicable<\/a> (and therefore not valid). We recognize emotions as euphoric, dysphoric, and fear. Theoretically, all emotions would be a combination of the three and so far in our model that\u2019s what we\u2019re seeing. We don\u2019t release a detector unless it\u2019s statistically significant as it relates to human-coder agreement.<\/p>\n

When you\u2019re dealing with peoples\u2019 emotions, you need it to be as reliable as possible.<\/p>\n

Word based analysis has proven to be the most reliable, as it is the labels we put on concepts and how we choose to codify the world around us. And then share it, with languages. Languages that require a common understanding. It does however, require special software that can extract latent clues from text.<\/p>\n

And there are some instances where the sentiment scores will match emotion recognition. Many of these sentiment analysis tools are sophisticated enough to get most of the polarity in the sentence. Which, if sentiment analysis and emotion recognition are accurate, should loosely agree.<\/p>\n

 <\/p>\n

\u201cI’m sorry and \u2018I apologize\u2019 mean the same thing… except when you’re at a funeral.\u201d<\/h3>\n

 <\/p>\n

\"\"Some of the leading sentiment analysis rated that sentence as \u201cnegative,\u201d which would be accurate. One of them rates it as moderately negative (-1, 0.5)<\/strong>. They agree. VERN rates that as 97% Humor, 80% Anger,<\/strong> and 66% Sadness, <\/strong>showing that it\u2019s probably a joke, that is both angry and sad.<\/p>\n

It\u2019s clear that the author was communicating \u201cdark\u201d humor, and admittedly it\u2019s a little risqu\u00e9. But what a great way to illustrate that there\u2019s more to life than just \u201cpositive,\u201d \u201cnegative,\u201d \u201cneutral,\u201d or \u201cmixed.\u201d<\/p>\n

Emotion recognition can add a lot to your analysis. It can tell you that there is an emotion present, which ones, how confident you should be about it, and how strong is the intensity of the emotion. So even when the methodologies arrive at a similar conclusion, it\u2019s not the same comparison.<\/p>\n

 <\/p>\n

It\u2019s never an apple-to-apple comparison<\/h1>\n

 <\/p>\n

It\u2019s more apple-to-orange. Both are fruits, but that\u2019s where the similarity lies. Emotion recognition can provide you with deeper insight into communications than sentiment analysis can. And, often times sentiment analysis doesn\u2019t agree or is just plain wrong.<\/p>\n

\u201cYep I\u2019m sad and now I wanna eat all of my feelings thanks.\u201d<\/h3>\n

 <\/p>\n

\"\"Two leading sentiment analysis tools rank that sentence as negative<\/strong>, and very positive<\/strong> (1, 0.7)<\/strong>. VERN analyzed the sentence and found that there was 51% Sadness, 51% Anger,<\/strong> and 44% Humor.<\/strong> To you and I, that would make sense. There is definitely a tone of anger and certainly sadness, though it\u2019s not a strong declaration of both.<\/p>\n

That\u2019s a clear difference. When you\u2019re talking about helping other human beings, such as in a healthcare setting, getting an objective and salient analysis is critical to assisting in making a diagnosis.<\/p>\n

Let\u2019s check out another example.<\/p>\n

\u201cPeople keep telling me life goes on, but that\u2019s the saddest part.\u201d<\/h3>\n

 <\/p>\n

Two leading sentiment analysis tools rank the above sentence as neutral<\/strong>, and a moderately<\/strong> strong positive. (1, 0.6). <\/strong>There\u2019s a clear disconnect between the sentiment tool, and the emotions that are actually present. Because use of language often means duplicity and multiplicity in the messaging, it\u2019s common to get these results. Some other software ranks this sentence as \u201cmixed;\u201d <\/strong>and while they weren\u2019t wrong. They just weren\u2019t right\u2026<\/p>\n

VERN ranked that sentence as 51% Sadness, 12.5% Anger,<\/strong> and 12.5% Humor<\/strong>. Again, this is a sad statement and emotion recognition would likely pick up on the emotives-the latent clues we use to communicate.<\/p>\n

It\u2019s so sad it\u2019s funny<\/h1>\n

 <\/p>\n

Sentiment analysis tools also have a problem identifying humor. While not a traditional emotion, the phenomena of humor spans multiple emotions and is an extremely powerful method of communication. It\u2019s one of the few that results in mostly demonstrable physiological responses. It\u2019s also one of the few communication phenomena that is universally practiced.<\/p>\n

So how can emotion recognition deal with humor? VERN\u2019s model is different and looks at humor as the detection of a benign incongruity. The sharing of this incongruity is the Sender\u2019s attempt at humor, and we\u2019ve created a detection model that measures over 20 signals that are statistically significant indicators of humor.<\/p>\n

\u201cI hate it when I go to hug someone really sexy and my face smashes right into the mirror.\u201d<\/h3>\n

 <\/p>\n

VERN scored that as 51% confidence of Humor<\/strong>. One of the leading sentiment analysis tools scored that as very negative (-1, 0.7). <\/strong>We can probably agree that the sentence was an attempt at humor, but I\u2019m not sure we\u2019d all agree that it\u2019s negative. (It\u2019s not something I have a problem with, for example).<\/p>\n

\u201cMy dad died when we couldn’t remember his blood type. As he died, he kept insisting for us to “be positive,” but it’s hard without him.\u201d<\/h3>\n

 <\/p>\n

VERN ranks that at 90% confidence of Humor,<\/strong> 33% Anger<\/strong>, and 51% Sadness<\/strong>. The sentiment analysis tools we compared ranked it as very negative<\/strong>. With an emotion recognition tool like VERN, you can find emotional clues even in humor-something that sentiment analysis can\u2019t do and very few emotion recognition tools do at all.<\/p>\n

As we can see, using emotion recognition software like VERN can help you find insights into communication that you might not otherwise find. It’s a look into the emotional state of the sender. It’s something that sentiment analysis struggles with, but emotion recognition products can help with.<\/p>\n

If you’d like to get started with VERN, head over to register for a free 30 day, 10,000 query free trial<\/a> or check out our other options<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"

In part one of our series, we compared sentiment analysis to emotion recognition in order to show the differences between the two approaches. We\u2019re back to do another comparison and talk a little about the methodologies and how they differ. First, a quick refresher: Sentiment analysis categorizes and analyzes text to determine if the writer\u2019s […]<\/p>\n","protected":false},"author":3,"featured_media":13268,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[73],"tags":[],"class_list":["post-13264","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog"],"yoast_head":"\nWhat to expect from your emotion recognition software Part 2 - VERN AI<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/host.tucknologies.com\/~v3rn4dm1n\/2021\/05\/25\/what-to-expect-from-your-emotion-recognition-software-part-2\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"What to expect from your emotion recognition software Part 2 - VERN AI\" \/>\n<meta property=\"og:description\" content=\"In part one of our series, we compared sentiment analysis to emotion recognition in order to show the differences between the two approaches. 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