AI News, The Microsoft India Blog The Microsoft India Blog

The Microsoft India Blog The Microsoft India Blog

 Detecting emotions in text is difficult enough for human beings, let alone artificially created machines, as many of our emotions are conveyed through expressions and tone of voice.

Here’s a look at how we arrived at a novel approach to detect emotions in textual conversations: The challenge of creating emotional Artificial Intelligence (AI) Detecting emotions accurately is always a challenge, even for human beings.

Although these gestures are subtle and easy to miss, they carry a wealth of information that can add context to the conversation.

A machine that can detect emotions can generate responses that genuinely help users seeking assistance or information.

Training wheels for AI High-quality and high-volume data combined with appropriate labels is essential when solving a machine learning problem like this.

The human judges used textual cues, smileys, emoticons, and punctuation as clues to detect emotions throughout the set.

For example, if a person responds to a text with “there, there,” it could convey a need to comfort someone which may indicate a sadness or despair in the previous text.

Once the human judges pruned this relatively small dataset, a nearest neighbour based clustering algorithm was used to automatically sort the larger set into respective categories.

As the name suggests, this Deep Learning model combines semantics and sentiment indicators to categorize text conversations based on the emotions they convey.

To train the model, we used the Microsoft Cognitive Toolkit and divided the data into ‘training’ and ‘validation’, based on a ratio of 9:1 (9 sets for training and 1 set for validation).

The results For each of the four emotion classes, our SS-LSTM model outperformed all other known state of art techniques for detecting emotions in textual conversations.

By creating a model that combines semantics and sentiment indicators in short, text-based conversations, our team has helped take a significant leap forward in detecting emotions.

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