Kanjoya’s proprietary technology goes beyond simple positive and negative sentiment to decipher emotions, human factors--including promotion, detraction, and activation--and nuance in language.
We integrate emotion analytics with structured demographic information (e.g., location, age, and gender) to provide rich, high-signal guidance that improves business results.
We benchmark your data against the statistical profiles for different data sources, enabling you to identify ‘hot spots’, unusual activity, and key trends in your business.
Built for massive scale, Kanjoya’s NLP technology automatically ingests vast sets of unstructured data, from Facebook and Twitter to surveys, search trends, and forum posts.
Moritz Sudhof, Chief Data Scientist at Kanjoya, manages the development of Kanjoya’s sentiment, emotion, and experience modeling technology.
His team, in collaboration with researchers at Stanford University, is leveraging the rich corpus of emotion data from Experience Project to model human expression in language.
Moritz discusses the world beyond sentiment analysis: context-aware emotion modeling for customer insights.
Sentiment Analysis relies on a simplistic classification system that only allows information to be designated as positive, negative, or neutral. These simple classifiers are often insufficient for anything but the most basic phrases. In contrast, Kanjoya Perception utilizes advanced natural language processing to identify a full range of emotions in unstructured text. Perception is able to handle nuance within polarity (positivity or negativity), conflicting polarity (the presence of both positivity and negativity), and promoter/detractor detection, and allows customers to understand and predict human behavior with a much higher degree of accuracy than sentiment analysis products.
Perception can handle any type of unstructured (open-ended) text as well as data integrated from multiple structured and unstructured sources. Common data types for Perception customers include open-ended survey responses, reviews from sites like Yelp, Trip Advisor, and mobile app stores, social sites, such as Facebook and Twitter, enterprise networks like Yammer, and text from help-desk systems. For the list of Perception’s integrations, please see question 13.
Perception includes a consumer-based UI to encourage a broad range of users across the enterprise. Training requires less than an hour, and the setup of new data streams generally requires less then 2 minutes.
Perception is built to handle vast amounts of data and can scale to handle the largest enterprise deployments. Dozens of Fortune 500 companies already rely on Kanjoya to support their marketing, customer experience, and employee engagement requirements.
Perception customers obtain meaningful results from datasets ranging from 500 rows to as high as five million social media messages. In general, the larger the dataset, the better.
EPS stands for Emotional Promoter Score and is similar to a Net Promoter Score (NPS). Like NPS, the score identifies promoters and detractors in your data, and is calculated with the following formula: %Promoters -% Detractors. Unlike NPS, however, EPS does not require a special survey–Perception automatically generates an EPS for all of your data, and for any segmentation thereof! This provides constant monitoring of the promoters and detractors in your data, and an understanding of the topics and themes driving their behavior.
Similar to NPS, good and bad scores typically vary across industries. In general, a good EPS is one that corresponds with an industry’s baseline NPS.
Customers may import any CSV or Excel file, as well as Medallia and other internal data sources, including email.
Perception’s integrations for enterprise social include Facebook at Work and Yammer. Social media integrations exist for Glassdoor, Twitter, Facebook, WordPress, and Blogs. Perception also integrates with the survey products Survey Monkey, Qualtrics, and Perceptyx, and reviews from the Apple App Store and Google Play. Finally, Perception integrates with news and discussion sites including message boards, Reddit, Topix, and IntenseDebate, as well as IMDB.
Additionally, Perception can distribute its own surveys and collect responses through the complete survey solution.
Kanjoya’s emotion models are statistical machine learning models that are trained on millions of documents. Each document consists of unstructured text–a comment, an exclamation, a review, a statement–paired with an emotion label that describes the affective content of the text. Crucially, it is the author of the unstructured text that provides the emotion labels; as a result, they are as accurate as possible and are based on genuine social connections about topics, ideas, and feelings. These emotion labels are far superior to the post-hoc labels assigned to text through annotation services such as Mechanical Turk (people give minimal thought to labeling because they are not personally invested in the data).
In other words, our models are essentially trained on millions of instances of real people telling us “I said ‘X’ and I was feeling emotion ‘Y'”, and our concept of an “emotion” is empirical as opposed to theoretical or rule-based. Each training document we have is another observation of an emotion “in the wild”, and our concept of what “annoyed” means, for example, is the result of millions of people voting and telling us what they believe “annoyed” means.
As is common for machine learning models, we evaluate our accuracy through held-out test sets, which are documents that are representative of our application domain and are not seen by our models during training. Each document contains a label, provided by the author, which describes the emotion of the text. To compute accuracy, we compare our models’ predictions for emotions to the emotions labeled by the authors of our test sets (please see FAQ #15 for more information regarding the creation of these models).