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Emotion Detection and Sentiment Analysis
Sentiment analysis and emotion detection are two NLP techniques
that use human language to categorise people's thoughts, attitudes,
and feelings. Sentiment analysis is a text classification technique that
determines if subjective information is favourable, negative, or neutral.
Emotion detection employs machine learning to examine complicated
emotions such as fear, anger, sadness, love, and frustration.
At first look, sentiment analysis and emotion detection may appear
to be the same concept particularly for individuals without a scientific background. However, they are not synonyms, so
what is the difference between sentiment analysis and emotion detection?
Factors Emotion Detection Sentiment Analysis
Definition Identifies various human emotion types. Measures the intensity of an emotion.
Seeks to identify the emotions expressed in Sentiment analysis seeks to categorise data as
Examples
texts, such as happiness, rage, and grief. positive, negative, or neutral.
Reading social media content, customer service
Applications Assessing user ratings, survey comments.
chats etc.
Uses a sliding scale between positive and negative
AI Training Can be taught to classify emotions. e.g. strongly disagree, disagree, neutral, agree, or
strongly agree.
Identifying emotional tokens to understand
Purpose Evaluating the overall tone or sentiment of text.
context.
Sentiment analysis focuses on the overall sentiment expressed, whereas emotion AI seeks to distinguish and
categorise distinct emotions. Thus, they work together to provide insights into the subjective components of human
communication.
Classification Problem
In Natural Language Processing (NLP), classification problems include assigning a label or category to a piece of text,
sentence, or phrase based on its content. For example, Emails can be classified as spam or non-spam, tweets as positive
or negative, and articles as relevant or irrelevant to a specific topic.
Human language often includes terms that are vague or have multiple meanings. This leads to a classification problem.
Understanding these terms can be challenging, especially for AI systems that rely on context to accurately classify given
elements within sentences. Let us understand this with the help of an example. Consider the following statements:
• He’s a really cool person. (Statement 1)
• The weather today is cool. (Statement 2)
In the above statements the phrases “cool person” and “cool weather” show the ambiguity of language, which poses a
classification problem. In statement 1, “cool person” can refer to someone who is admired for their style or demeanour.
Whereas in statement 2, a “cool temperature” refers to a lower, bearable level of heat. The challenge arises because the
word “cool” has been used in two different ways.
Just as with humans, it can be tricky for an AI system to correctly interpret these meanings without proper context. Let’s
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