detect emotion from text

Presented By :
Md. Safayet Hossain
CSE of Daffodil International University
2

Designing A English Language
Compiler :
English To Emotion
Detection
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Keywords :
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 Abstract
 Language Processing
 Emotion Estimation
 Software Requirements
 Challenges
 Future Work
 Reference

“What is emotion?
▸ A strong feeling deriving from one's circumstances, mood, or relationships
with others.
▸ Emotions are complex. According to some theories, they are a state of feeling
that results in physical and psychological changes that influence our behavior.

Types of Emotion :
Generally we use 3 types of emotion model to detect emotion from text.
These are-
1. Ekman (anger, disgust, fear, joy, sadness, and surprise)
2. Izard (anger, contempt, disgust, distress, fear, guilt, interest, joy, shame,
and surprise)
3. Plutchik (anger, anticipation, disgust, fear, joy, sadness, surprise, and
trust)
These are common lists of emotions used in emotion detection methods.
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Abstract :
Detecting emotion from text is a relatively new classification task and advancements in
textual analysis have allowed the area of emotion detection to become a recent interest in the
field of natural language processing. There is still a question on how to detect emotion from a
text input. To solve this problem, this project generates an Emotion Detection Model to
extract emotion from text at the sentence level.
Our method detects emotion from a text-input by searching direct emotional key words from
that input. To make the detection more accurate, emotion-affect-bearing words and phrases
were also analyzed.
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Architecture of Emotion
Detection :
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Language Processing :
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Natural language processing (NLP) is the computerized approach to analyze text that is
based on both a set of theories and a set of technologies. It is concerned with the
interactions between computers and human (natural) languages. NLP is presenting
naturally occurring texts at one or more level of linguistic analysis for the purpose of
achieving human-like language processing for a range of tasks or applications.
NLP has two major methods of analysis.
 Keyword Analysis or Pattern matching technique.
 Syntactic driven parsing technique.

Keyword Analysis :
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In keyword analysis or Pattern matching technique, the system scans the input sentences
for “selective” keywords and once they are encountered, the system responds with a
‟built-in” reply.

Parsing :
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Parsing is the process of analyzing a sentence by taking it apart word-by-word and determining
its structure from its constituent parts and sub-parts. Parsing would seem to be a rather easy
mechanical process. Given a lexicon telling the computer the Parts of Speech for a word, the
computer would be able to just read through the input sentence word by word and in the end
produce a structural description. But problems arise for several reasons. First of all, a word may
function as different parts of speech in different contexts. For example: “He wanted some oil
for his bicycle” Here, the word “oil” treats as a noun, whereas “This is an oil painting.” Here,
the word “oil” treats as an adjective. So it is not possible to know how the word “oil” is used
until we read the entire sentence. So we have to determine which Parts of Speech is relevant in
the particular context at hand.

Example :
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A word may function as different parts of speech in different contexts.
For example:
“He wanted some oil for his bicycle”
Here, the word “oil” treats as a noun,
whereas “This is an oil painting.”
Here, the word “oil” treats as an adjective.
So it is not possible to know how the word “oil” is used until we read the entire sentence.
So we have to determine which Parts of Speech is relevant in the particular context at hand.

Semantic Analysis :
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Semantics and its understanding as a study of meaning covers most complex tasks like:
finding synonyms, word sense disambiguation, constructing question-answering
systems, translating from one NL to another, populating base of knowledge.
Basically one needs to complete morphological and syntactical analysis before trying to
solve any semantic problem.

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Emotion detection is modeled as a classification problem where one or more nominal labels
are assigned to a sentence from a pool of target emotion labels.
Our emotion detection framework contains two main modules:
1. Word-processing module.
2. Sentence Analysis.
The module named sentence analysis which is an easy but a lengthy process.

Word-processing Module :
our office
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89,526,124$That’s a lot of money
100%Total success!
185,244 usersAnd a lot of users
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Sentence Analysis :
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In this sentence analysis module, our aim is to detect emotion from a sentence where there
is no emotional keyword in the sentence. For this purpose, we analyze different categories
of sentence.
Detecting emotion from exclamatory sentence: Any sentence expressing sudden emotion
is called exclamatory sentence. Exclamatory words that can stand alone as a sentence while
expressing emotions or reactions are called interjections. Interjections don’t require a
subject or verb to express a thought. They can be inserted in a sentence by using commas.
For example:
Wow, that was a thrilling ride!
Brilliant, you solved the puzzle!

“Software Requirements :
 Python ,C++
 Wordkit Library.
Tools:
 Pycharm,
 Jupyter notebook,
 any text editor.

Challenges :
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Keyword
Selection
Sentiment
Domain

Future Work :
The future of emotion detection is promising. Although not enough time has spent to have
established standards in this field, the algorithms are continuing to increase in accuracy.
In future we will try to increase the resources of our affect lexicon and emotional dataset
to increase the performance of our methods as well as to increase the accuracy of the
entire system. There are many advantages in being able to identify emotion from text
input.
To being able to build such kind of applications, the ability to detect emotion from text
can enhance the human-computer interaction. If the computer can tell a person’s mood or
emotional state, it would be able to switch to an accommodating form of interaction.
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References:
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 http://blog.paralleldots.com/product/emotion-detection-using-machine-learning/
 https://indico.io/blog/recognizing-emotion-in-text-machine-learning-no-code/
 https://socialmediaweek.org/blog/2017/08/4-machine-learning-emotion-detection-apis-
need-try/
 https://www.slideshare.net/Loveysliet/emotion-mining-in-text
 https://www.quora.com/What-kind-of-tool-for-emotion-detection-should-I-use

detect emotion from text

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