Social media networks have become a primary source for news and opinions on topics ranging from sports to politics. Sentiment analysis is typically constrained to two classes—positive and negative. A computing system is herein described for building a multi-sentiment multi-label model for electronic data that uses emojis as class labels. The electronic messages are classified into six sentiment classes. The computing system collects and creates a large corpus of clean and processed training data with emoji-based sentiment classes using little-to-no manual intervention. A threshold-based formulation is used to assign one or two class labels (multi-label) to an electronic message. The multi-sentiment multi-label model produces a desirable cross validation accuracy.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computing system comprising: a communication device to automatically obtain electronic messages having emojis; a memory device to store the electronic messages and one or more classifiers configured to identify n emoji classifications; one or more processors to at least: classify the electronic messages using the one or more classifiers into the n emoji classifications; remove p classifications from the n emoji classifications that are characterized by a value lower than a given threshold; classify electronic messages remaining in the (n-p) emoji classifications; output the classifications of the electronic messages remaining in the (n-p) emoji classifications. 2. The computing system of claim 1, wherein the one or more processors pre-process the electronic messages before classifying the electronic messages. 3. The computing system of claim 1 wherein the memory device further comprises a Word2Vec neural network, and the one or more processors at least: obtain an initial set of electronic messages, each one having one or more emojis; automatically label each one of the electronic messages in the initial set using the one or more emojis; training the Word2Vec neural network to with the labelled electronic messages; and using the trained Word2Vec neural network to cluster emojis in the initial set of electronic messages into the n classifications.