E-commerce17 cols · 200 rows
This dataset provides a comprehensive view of customer product reviews, including detailed ratings, review text, sentiment analysis, and user demographics such as age, gender, and location. It enables advanced sentiment analysis, natural language processing, and customer satisfaction research, making it ideal for businesses seeking to understand product perception and improve customer experience.
| review_title | verified_purchase | sentiment_label |
|---|
| Great Value! | true | positive |
| Did not work as expected | true | negative |
| It’s okay | false | neutral |
review_idcustomer_idproduct_id+11 more
E-commerce12 cols · 200 rows
This dataset contains detailed e-commerce product reviews, including review text, star ratings, sentiment labels, and metadata such as helpful votes and verified purchase status. It is ideal for training and evaluating sentiment analysis models, understanding customer feedback, and powering recommendation engines. The rich structure supports both NLP research and practical business applications.
| product_name | review_title | sentiment_label |
|---|
| Wireless Bluetooth Earbuds | Amazing Sound Quality! | positive |
| Stainless Steel Water Bottle | Keeps Drinks Cold For Hours | positive |
| Foldable Laptop Stand | Makes Working Easier | positive |
review_idproduct_iduser_id+6 more
E-commerce13 cols · 200 rows
This dataset provides detailed customer product reviews, including star ratings, review text, helpfulness votes, verified purchase status, and tracked responses from sellers or support teams. It enables comprehensive sentiment analysis, product feedback evaluation, and customer engagement tracking for e-commerce platforms.
| review_title | verified_purchase | response_author |
|---|
| Absolutely fantastic! | true | SupportTeam |
| Works as expected | true | – |
| Not worth the price | true | QA_Manager |
review_idproduct_idcustomer_id+7 more
Social Media14 cols · 200 rows
This dataset contains synthetic social media posts and their associated comments, each labeled with sentiment (positive, negative, neutral) and optional sentiment scores. It is designed for training and benchmarking machine learning and NLP models in social analytics, enabling fine-grained sentiment analysis across multiple platforms and languages.
| post_sentiment_label | comment_sentiment_label | platform |
|---|
| positive | positive | twitter |
| positive | positive | facebook |
| positive | positive | instagram |
post_iduser_idpost_datetime+8 more
Education13 cols · 200 rows
This dataset provides detailed student feedback on courses, enriched with sentiment analysis labels and scores, course and instructor metadata, and student academic context. It enables educators and administrators to systematically analyze curriculum effectiveness, identify areas for improvement, and track sentiment trends over time for data-driven decision making.
| sentiment_label | course_title | instructor_name |
|---|
| positive | Introduction to Computer Science | Dr. Alicia Thornton |
| neutral | Principios de Economía | Prof. Ricardo Alvarez |
| neutral | 数据结构基础 | Dr. Li Wei |
feedback_idstudent_idcourse_id+7 more
Finance13 cols · 200 rows
This dataset aggregates real-time sentiment scores and metadata for financial news headlines, enabling rapid detection of market-moving events and trends. It includes headline text, publication details, sentiment analysis, relevance to financial markets, and links to affected stocks and sectors. Ideal for quantitative trading, risk monitoring, and financial news analytics.
| source_name | sentiment_label | event_type |
|---|
| Reuters | positive | earnings |
| Handelsblatt | neutral | macroeconomic |
| CNBC | positive | earnings |
headline_idheadline_textpublished_datetime+7 more
Healthcare11 cols · 200 rows
This dataset provides detailed, synthetic healthcare chatbot conversations with annotated intent labels, message sequencing, and extracted entities. Designed for training and evaluating conversational AI, it supports intent classification, dialogue modeling, and entity recognition in healthcare virtual assistants. The dataset enables robust analysis of user-bot interactions for improved patient engagement and automation.
| sender_type | intent_label | conversation_topic |
|---|
| user | book_appointment | appointments |
| bot | request_time | appointments |
| user | provide_time | appointments |
transcript_idmessage_idmessage_order+5 more
Technology12 cols · 200 rows
This dataset contains synthetic, multi-domain user feedback entries, each labeled for sentiment, theme, and actionability to support machine learning applications in automated response systems and product improvement. It enables granular analysis of user concerns, trends, and actionable insights across different product areas, making it ideal for customer experience optimization and workflow automation.
| sentiment_label | theme_label | actionability_label |
|---|
| positive | feature_request | actionable |
| negative | performance | actionable |
| mixed | support | needs_clarification |
feedback_iduser_iddomain+6 more
Technology20 cols · 200 rows
This dataset contains detailed records of IT service tickets, combining structured metadata (such as priority, category, and assignment) with rich ticket descriptions suitable for natural language processing. It enables automated ticket triage, prioritization, and advanced analytics for IT support operations, making it ideal for machine learning and process optimization.
| ticket_subject | category | priority |
|---|
| Laptop battery fails to charge | hardware | medium |
| Cannot print from office printer | hardware | high |
| Server room temperature alert | hardware | critical |
departmentstatusurgency+14 more