EMOTION DETECTION USING TEXT & FACE

 EMOTION DETECTION USING TEXT & FACE

Emotional Detection as a process of identifying human emotion from any form of written text.

Due to the advanced use of NLP, machine learning, and computational linguistics for extracting emotion and satisfaction relevance in text analysis, this tool has become a prevalent topic for research studies.

While Sentiment Analysis (link) seeks to obtain a polarity from the text, that is, to gauge if a sentiment expressed is positive, negative, or neutral, Emotion Analysis takes it a step further.

It provides us an insight into the underlying reasons for the sentiment output: happy, sad, angry, confusion, curiosity, and so on.

For example, here are three sentences:

● I want to return this product, maybe order a replacement.

● This is not a friendly product.

● I hate this product; not useful at all.

Although all three sentences deliver us with the same sentiment, which is negative, when we examine the emotion behind each sentence, it is quite different.

According to this, the approach to customer service would also be very different and should be handled by marketers and client support teams in distinct ways.

What is the significance of using Emotion Analysis?

During interactions over digital media, emotions become a significant component in the communication between people of different cultural languages.

Generally, we can divide emotions into six types which are:

joy, surprise, love, anger, sadness, and fear.

In the context of written text, emotions may be expressed by a single word, for example,

- Great.

- Nope.

- Recommend.

Or it can be a group of words,

- This is great!

- Nope, won’t buy again!

- Definitely recommend this product!

Thus, phrase- and sentence-level emotion analysis methods play a vital role in tracking emotions or questing the indications for recognizing emotions.

A document always consists of sentences that have words or phrases.

The detection of emotions from word level, phrase level, and document level is very complex.

But at the same time, it is highly crucial for an accurate simulation of customer behavior.

The fast-flowing internet services have facilitated increased online communication and written content over the websites.

That has led to the flow of large amounts of online content rich in user opinions and varies across digital platforms.

The exchange of emotions through plain text, messages, tweet posts, comments, etc.,

Emotion Detection will play a promising role in the field of Artificial Intelligence, especially in the case of Human-Machine Interface development.

For Emotion Detection from an artificial intelligence, different parameters should be taken into consideration.

Various types of techniques are used to detect emotions from a human being like facial expressions, body movements, blood pressure, heart beat and textual information.

This paper focuses on the emotion detection from textual information There are four basic methods to detect emotions from text:

1) Keyword based detection,

2) Learning-based detection,

3) Lexical affinity method,

4) Hybrid detection.

Each and every method contains some strong and weak points while detecting emotions from text.

Hybrid Method is the most likely method to get a high accuracy result, as it includes the combined strength of two or more methods.

The main difficulty is to find the most effective combination.

Tools we need :-

Anaconda :

Packages of multiple libraries and IDEs IDEs

Jupyter notebook VSCode

Install these libraries (we need to install after anaconda being installed)

1)Package NRCLex (lexicon-based methods) NRC = National Research Canada

Pip install NRCLex

2)DeepFace (for deep learning models implementation) - Pre-trained models

Pip install deepface 3)Opencv

Pip install opencv-python

Examples or Applications

There are four basic methods to detect emotions fromtext:

1)Keyword- based detection,

2)learning-based detection,

3)lexical affinity method,hybrid detection.

Each and every method contains some strong and weak points while detecting emotions from text.

It uses facial expressions to identify parts of an image or video to determine age, gender and emotions.

This technology can be applied to fields like security, biometrics, law enforcement, etc., for tracking and surveillance purposes.