The document discusses new methodologies for identifying customer needs using user-generated content (UGC) in the context of e-commerce, particularly influenced by the COVID-19 pandemic. It outlines goals to improve analysis and encoding of review qualities while addressing biases in review data. The proposed methodology includes data gathering, preprocessing, feature engineering, and model evaluation to enhance the usefulness and accuracy of insights derived from customer reviews.

1. NEW METHODOLOGIES FOR
IDENTIFYING CUSTOMER NEEDS
FROM USER-GENERATED CONTENTS

2. Introduction
• COVID-19
• E-Commerce
• Traditional Methods
• User-Generated Content (UGC)
• Marketing Issues
• NLP in Marketing

3. Research Gaps
• Review-based Recommendation Approaches
• Biased Reviews
• Imbalanced Classification Problem
• Context-dependent Customer Demands
• Best Class Weight for Minority Groups

4. Goal and Objectives
• Creating processes that allow businesses to keep an eye on the
constantly changing User Generated Contents
• To improve analysts capacity to deduce what customers require from
educational and non-repetitive user-generated content.
• To introduce and create new methods for encoding the qualities of
reviews to improve their usefulness by including more details.

5. Goal and Objectives
• To create models for different review qualities so that knowledge of
reviews can be enhanced according to various reviews' features.
• To provide an appropriate evaluation measure or procedure for
minimizing potential biases so that accurate and fraudulent review
data may be distinguished to lessen social injustice or inequality
issues.

6. Methodology
• Data Gathering
Reviews data for chosen companies will be crawled, and event-based data will
be gathered via events calendars.
• Data Pre-Processing
1. Data Cleaning
2. Tokenization
3. Stop word removal
4. Stemming
5. POS tagging

7. Preprocessing- Example

8. Methodology
• Features Engineering
• Applying words Embedding
technique
1. Word2Vec
2. fastText
• Identify Educational Content
We would categorize sentences into small groups as informative or non-
informative, train a CNN, and use it to remove non-informative sentences from
the rest of the corpus.

9. Methodology
• Datasets Splitting
1. Training Set (80%)
2. Testing Set (20%)
• Sample Contents with Variety
• Assessment and Validation
Following tools will used to validate the models
Accuracy
Precesion
Recall
F1- Score

10. Conclusion