Computing Systems and Methods for Determining Sentiment Using Emojis in Electronic Data

• US 20170364797A1
• Filed: 06/15/2017
• Published: 12/21/2017
• Est. Priority Date: 06/16/2016
• Status: Abandoned Application

• Current Assignee: Meltwater News International Holdings GmbH (Meltwater NV)
• Original Assignee: Sysomos L.P.
• Inventors: PAL, Koushik, PADMANABHAN, Kanchana, MAYANK, Dhruv
• Application Number:
  US15/624,100
• Publication Number:
  US 20170364797A1
• Time in Patent Office: Days
• CPC Class Codes: G06F 40/20 Natural language analysis semantic analysis of natural language G06F40/30
• CPC Class Codes: G06N 3/08 Learning methods
• CPC Class Codes: G06Q 50/01 Social networking

Abstract

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.

Claims

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.