1) The document proposes using text-based emotion detection applied to the consumer goods industry to gauge customer responses, monitor advertising campaigns, and guide product development. 2) The proposed approach uses a transformer-based model with emotion-aware preprocessing and domain knowledge insertion to classify emotions into classes with percentages and explanations. It casts emotion classification as span prediction and uses label-correlation losses. 3) The proposed outputs include generating product analytics based on scraped data, explaining a text's emotions using the model, and testing the model's efficacy on user-entered texts.

1. Sigmoid Hackathon
Idea Submission Phase

2. —Sherlock Holmes in “A study in Scarlet”
“It is a capital mistake to theorize before one has data.”

3. Meet the team :Young_Monks
Soumyo Bhattacharjee
Fourth Year Undergraduate Students
Indian Institute of Technology, Kharagpur
Shrinivas Khiste Shivam Pandey
Vishal Kumar Harsh Sharma

4. Breakdown of the
Problem and Impact
Strategies to tackle the
problem statement
01 02
Overview of
Problem Statement
High Level
Approach
Details regarding
technology stack needed
Use Cases / Proposed
Final Outlook
03 04
Requisite
Technologies
End Demonstrable
Output
Table of contents

5. Overview of Problem
Statement
01

6. Emotion Detection
Emotion Detection , is the synergistic association of emotions also
and technology. Emotion detection seeks to extract finer-grained
emotions such as happy, sad, angry, and so on, from human
languages rather than coarse-grained and general polarity
assignments in Sentiment Analysis, in order to provide fine-grained
decision-making.
Emotions play a very vital role in decisions that people make. To
understand these emotions can open new opportunities for any
business including the consumer goods industry.
Emotions can form the basis to provide recommendations and
improve customer experience at every touchpoint, in-turn
maximizing revenue for the company.

7. Why Text-Based Emotion Detection ?

8. $32.95 Billion
Expected to Grow at 16.7% CAGR , 2022 - 2030
Global Emotion Detection and Recognition Market Size

9. Use Cases: Consumer Goods Industry
Monitor effectiveness of
Advertising Campaigns
User Experience Monitoring
Product Development
Strategy
Products
Customer
Acquisition and
Retention
Taking inspiration from both of the Problem Themes of the Hackathon, a interesting domain was taken
into consideration : Text Based Emotion Detection applied to the Consumer Goods Industry
Gauge Response to New
Products

10. High Level Approach
You can enter a subtitle here if you need it
02

11. Overview
TRANSFORMER
BASED
MODEL
Domain
Knowledge
Insertion 1
Emotion
Classes
Explainable AI
Module
Output
Emotions
(with %)
Explanation
Emotion Aware
Preprocessing
1. Span Prediction Loss 2
2. Label Correlation Aware Loss 3
( process emotion affecting
expressions like emojis,
deliberate misspellings,
abbreviations,etc )
1. LIME Explainer 4
2. Visualisation of hidden
representations

12. Salient Features
Semantics of emotion labels
guide models attention to
generate contextualised
input representations
Acknowledge emotion
affecting expressions while
preprocessing input
1. Contextualised
input representations 2
2. Emotion Aware
Preprocessing
Casting multi-label emotion
classification as span
prediction for better
representation and
performance
Model co-existence of
multiple emotions and track
label-label correlation to
penalise incongruous
predictions
4. Span Prediction
Loss 2
5. Label Correlation
Aware Loss 3
Learning domain specific
patterns from text and
inserting it to the model to aid
classification
3. Domain Knowledge
Insertion 1
Explain the output of the
model to better guide product
development strategy
6. Explainable AI 4

13. References
1. Ying, W., Xiang, R., & Lu, Q. (2019). Improving Multi-label Emotion Classification by
Integrating both General and Domain-specific Knowledge. In Proceedings of the 5th
Workshop on Noisy User-generated Text (W-NUT 2019) (pp. 316–321). Association
for Computational Linguistics.
2. Alhuzali, Hassan & Ananiadou, Sophia. (2021). SpanEmo: Casting Multi-label
Emotion Classification as Span-prediction. 1573-1584.
10.18653/v1/2021.eacl-main.135.
3. Gaonkar, R., Kwon, H., Bastan, M., Balasubramanian, N., & Chambers, N. (2020).
Modeling Label Semantics for Predicting Emotional Reactions. In Proceedings of the
58th Annual Meeting of the Association for Computational Linguistics (pp.
4687–4692). Association for Computational Linguistics.
4. Garreau, D., & Luxburg, U.. (2020). Explaining the Explainer: A First Theoretical
Analysis of LIME.

14. Requisite Technologies
03

15. Backend Frontend

16. End Demonstrable Output
04

17. Generate Insights based on Scraped
Data to provide an overview
User Entered Text is used to explain the
functioning of the Model used in generation of
analytics
Product Based Analytics
Model Efficacy Testing
Planned Outputs to demonstrate

18. Front-End Website to Generate Analytics
—- Select — Generate
Product Analytics

19. Front-End Website to Test NLPBackend
The product A is very well made. I have been using it for 2 months and
so far it has exceeded my expectations. It is very reasonable.I am
happy with my purchase and would recommend it to others.
Submit
Test Model Efficacy !
The product A is very well made. I
have been using it for 2 months and
so far it has exceeded my
expectations. It is very reasonable.I
am happy with my purchase and
would recommend it to others.
Top 3 Emotions

20. ThankYou!