A comprehensive dataset for sentiment and emotion classification from Bangladesh e-commerce reviews
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View abstract on PubMed
Summary
This summary is machine-generated.This study analyzes Bangladeshi e-commerce reviews to understand consumer emotions and preferences. The findings offer valuable insights for improving products and guiding future development in the dynamic Bangladeshi market.
Area Of Science
- E-commerce and Consumer Behavior Analysis
- Natural Language Processing (NLP) Applications
- Sentiment Analysis in Digital Markets
Background
- Customer feedback analysis is vital for e-commerce success, especially in emerging markets like Bangladesh.
- Existing research often lacks nuanced understanding of consumer sentiment in diverse linguistic contexts.
- Bangladeshi e-commerce platforms present a unique landscape for studying consumer emotions and preferences.
Purpose Of The Study
- To create and analyze a comprehensive dataset of customer reviews from Bangladeshi e-commerce platforms.
- To classify emotions and sentiments expressed in Bengali and English reviews, including those with emojis.
- To provide actionable insights for product enhancement and development within the Bangladeshi market.
Main Methods
- Data collection from two major Bangladeshi e-commerce platforms (Daraz, Pickaboo).
- Linguistic analysis of reviews in both Bengali and English, incorporating emoji sentiment.
- Expert annotation of reviews into five emotion categories (Happiness, Sadness, Fear, Anger, Love) and sentiment polarity (Positive, Negative).
Main Results
- A rich dataset of diverse customer reviews was compiled and annotated.
- Detailed classification of emotions and sentiments provides a nuanced view of consumer feedback.
- Identification of specific emotional drivers influencing purchasing decisions in Bangladesh.
Conclusions
- The dataset serves as a valuable resource for sentiment analysis and NLP research.
- Insights derived enhance understanding of unique consumer behavior patterns in Bangladesh.
- Findings support data-driven strategies for e-commerce growth and customer satisfaction in the region.
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