This dataset comprises customer reviews for Amazon, an online retail giant, featuring insights into customer experiences, including ratings, review titles, texts, and metadata. It is valuable for analyzing customer satisfaction, sentiment, and trends.

Column Descriptions:

Reviewer Name: Identifies the reviewer. Profile Link: Links to the reviewer's profile for additional insights. Country: Indicates the reviewer's location. Review Count: Number of reviews by the same user, showing engagement level. Review Date: When the review was posted, useful for time analysis. Rating: Numerical satisfaction measure. Review Title: Summarizes the review sentiment. Review Text: Detailed customer feedback. Date of Experience: When the service/product was experienced.

Prospective applications:

Sentiment Analysis: Analyze review texts and titles to assess overall customer sentiment toward products, enabling the identification of strengths and weaknesses. Customer Satisfaction Tracking: Track and visualize rating trends over time to understand fluctuations in customer satisfaction. Product Improvement: Identify common themes in reviews to highlight areas for product enhancement or development. Market Segmentation: Use country and demographic information to customize marketing strategies and gain insights into regional preferences. Competitor Analysis: Evaluate customer feedback on Amazon products in comparison to competitors to determine market positioning. Recommendation Systems: Leverage review data to enhance recommendation algorithms, improving personalized shopping experiences. Trend Analysis: Investigate temporal patterns in reviews to link sentiment changes with marketing efforts or product launches.

This extensive dataset serves as a valuable asset for various analyses focused on enhancing customer engagement and refining business strategies.

Dataset Card for amazon reviews for sentiment analysis

  Dataset Summary

One of the most important problems in e-commerce is the correct calculation of the points given to after-sales products. The solution to this problem is to provide greater customer satisfaction for the e-commerce site, product prominence for sellers, and a seamless shopping experience for buyers. Another problem is the correct ordering of the comments given to the products. The prominence of misleading… See the full description on the dataset page: https://huggingface.co/datasets/hugginglearners/amazon-reviews-sentiment-analysis.

Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.

Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).

Each Dataset contains the following columns : marketplace - 2 letter country code of the marketplace where the review was written. customer_id - Random identifier that can be used to aggregate reviews written by a single author. review_id - The unique ID of the review. product_id - The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id. product_parent - Random identifier that can be used to aggregate reviews for the same product. product_title - Title of the product. product_category - Broad product category that can be used to group reviews (also used to group the dataset into coherent parts). star_rating - The 1-5 star rating of the review. helpful_votes - Number of helpful votes. total_votes - Number of total votes the review received. vine - Review was written as part of the Vine program. verified_purchase - The review is on a verified purchase. review_headline - The title of the review. review_body - The review text. review_date - The date the review was written.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('amazon_us_reviews', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

This dataset provides a free trial sample of best-selling products and their customer reviews from a leading e-commerce platform, designed to support product intelligence, sentiment analysis, and market trend evaluation. This sample is provided for evaluation purposes only. It includes a curated subset of the full dataset. To access the complete dataset, request additional attributes, or explore alternative product segments, please contact the data provider directly.

  Key Features

2… See the full description on the dataset page: https://huggingface.co/datasets/datahiveai/Amazon-Reviews-Dataset.

Overview: This dataset contains a subset of Amazon customer reviews from the "Cell Phones & Accessories" category. The dataset provides valuable insights into customer sentiment and opinions related to various cell phone and accessory products available on Amazon. Whether you're interested in natural language processing, sentiment analysis, product recommendations, or market research, this dataset can be a valuable resource.

Context: With the ever-increasing variety of cell phones and accessories available online, understanding customer feedback and preferences is crucial for businesses, researchers, and data enthusiasts. This dataset offers a glimpse into customer sentiments regarding different products, allowing for a wide range of analytical and research applications.

License: Please note that this dataset is for research and analysis purposes only and may be subject to copyright and terms of use from Amazon. Make sure to comply with Amazon's policies when using this data.

Dataset Source: The original dataset was scraped from Amazon's website.

Amazon Product Review Dataset (2023)

  Dataset Overview

The Amazon Product Review Dataset (2023) contains product reviews from Amazon customers. The dataset includes product information, review details, and metadata about the customers who left the reviews. This dataset can be used for various natural language processing (NLP) tasks, including sentiment analysis, review prediction, recommendation systems, and more.

Dataset Name: Amazon Product Review Dataset (2023) Dataset… See the full description on the dataset page: https://huggingface.co/datasets/kevykibbz/Consumer_goods_reviews.

This dataset was created from the scraped reviews from products in Amazon for the purpose of text classification. The classes are three in number namely; - Negative Reviews - Neutral Reviews - Positive Reviews

Data columns includes; - Sentiments - Cleaned Review - Cleaned Review Length - Review Score

This dataset presents the problem of multiclass classification with the use of ML algorithms and also deep learning algorithms. Moreover, there is a class imbalance; negative reviews has the lowest number of reviews compared to positive and neutral reviews.

For ML algo use a mapping of; negative--> -1, neutral--> 0, positive --> 1

For Deep Learning algo use a mapping of; negative --> 0 neutral --> 1 positive --> 2

Looking forward to your model discoveries on this dataset.

Please leave an upvote if you find this relevant 😀.

Dataset Card for "Amazon Food Reviews"

  Dataset Summary

This dataset consists of reviews of fine foods from amazon. The data span a period of more than 10 years, including all ~500,000 reviews up to October 2012. Reviews include product and user information, ratings, and a plain text review. It also includes reviews from all other Amazon categories.

  Supported Tasks and Leaderboards

This dataset can be used for numerous tasks like sentiment analysis, text… See the full description on the dataset page: https://huggingface.co/datasets/jhan21/amazon-food-reviews-dataset.

OpenWeb Ninja's Amazon Data API provides fast and reliable access to real-time Amazon data across all 22 Amazon domains. With over 600 million product listings and more than 40 data points per product, the API makes it simple to search products, query by category, and extract structured ecommerce product data at scale.

Key capabilities: - Product Search & Categories: search Amazon by keyword or retrieve products directly from categories. - Product Data: titles, descriptions, images, pricing, availability, attributes. - Amazon Reviews Data: full review content, ratings, timestamps, helpful counts. - Offers & Sellers Data: all current offers, with sellers data, and more. - Amazon Sellers Data: Amazon sellers profile, sold products, and seller reviews. - Best Sellers & Deals: Amazon Best Sellers by category, Today’s Deals, and promotions. - ASIN to GTIN: convert ASIN to GTIN/EAN/ISBN for external integrations.

Coverage & Scale: - 600M+ products across all major categories and industries. - 22 Amazon countries/domains supported. - 40+ structured data points per product. - Real-time updates, delivered via a fast and reliable REST API.

Use cases: - Pricing and product comparison tools. - Ecommerce and market research. - Seller and competitor monitoring. - Product discovery and trend analysis. - Sentiment analysis with customer product reviews data.

With OpenWeb Ninja's Amazon Data API, you get the most complete Amazon data - from product details and reviews to best sellers and deals - always delivered in real time through a fast and reliable REST API.

This dataset provides a comprehensive collection of 79,821 end-user reviews from 64 low-rated software applications on the Amazon Appstore. The dataset is specifically curated to focus on applications that exhibit significant user dissatisfaction, making it a valuable resource for studying the root causes of software failure and negative user reception. The applications span 14 distinct categories, offering a broad view of user feedback across different software domains.

The data was systematically collected using an automated web scraping tool. The selection criteria targeted applications with a user rating of 3 stars (out of 5) or lower and a minimum of 400 reviews to ensure the data reflects a broad consensus of user opinion. The dataset is provided in CSV format and contains three primary columns for each review: the user's rating ('Stars'), the 'Title_of_Review', and the full text of the 'Base_Review'.

This resource is intended for researchers, software developers, requirements engineers, and educators. It is particularly useful for studies in software quality assessment, user experience (UX) analysis, sentiment analysis, issue detection, and for training and validating machine learning and natural language processing (NLP) models. Its unique focus on negative feedback provides an unfiltered and concentrated source of data for understanding why software products fail to meet user expectations.

File includes 4605 reviews for a high quality dog food product on Amazon. This dataset was generated using Unwrangle Review Extractor API.

This dataset can be used for the following applications and more:

** Analyzing trends**

Just as an example, you can see estimate how room occupancy must have been affected by the Covid 19 pandemic.

** Sentiment Analysis / Opinion Mining**

Using NLP techniques one can find out what the average user’s sentiment is towards each of the featured hotels in this dataset.

** Topic / Aspect Extraction**

Using categorization techniques one can quickly figure out how each of the hotels featured in this dataset fairs on attributes such as room quality, staff, food, check-in process, etc.

** Competitor Analysis**

If you would like to find out what customers think about your competitors, a tailored dataset like the one featured in this blog post can enable you to do so with simple data analysis or visualization techniques.

Code frequencies for Amazon consumer reviews.

Codes applied to Amazon reviews.

Consumer behavior has changed due to digitization. Online shoppers now refer to user reviews containing comprehensive data produced in real-time, which can be used to determine users’ needs. This paper combines Kansei engineering and natural language processing techniques to extract information on users’ needs from online reviews and provide guidance for subsequent product improvements and development. A crawler tool was used to collect a large number of online reviews for a target product. Frequency analysis was then applied to the text to filter out the product components worth analyzing. The results were categorized and aggregated by experts before sentiment analysis was performed on statements containing the selected adjectives. Finally, the user needs identified could be inputted to Kansei engineering for further product design. This paper verifies the merit of the above method when applied to the mountain bike product category on Amazon. The method proved to be a quick and efficient way to attain accurate product evaluations from end-users and thus represents a feasible approach to intelligently determining user preferences.

Final sample for each genetic company by number of homepages, test pages, and Amazon reviews.

The APAC retail analytics market, valued at $9.28 billion in 2025, is experiencing robust growth, projected to expand at a Compound Annual Growth Rate (CAGR) of 14.43% from 2025 to 2033. This surge is driven by the increasing adoption of data-driven decision-making strategies among retailers in the region. E-commerce expansion, the rising need for personalized customer experiences, and the proliferation of advanced technologies like AI and machine learning are key catalysts. The market is segmented by deployment mode (on-premise and on-demand), type (solutions and services), module type (strategy, marketing, financial management, store operations, merchandising, supply chain, and others), business type (SMEs and large-scale organizations), and geography (China, India, Japan, and South Korea). The on-demand segment is witnessing faster growth due to its scalability and cost-effectiveness. Services, particularly integration, support, and consulting, are in high demand as retailers need assistance in implementing and leveraging these analytics solutions. Large-scale organizations are currently the major consumers, however, the SME segment is poised for significant growth, driven by increasing affordability and accessibility of cloud-based solutions. While data privacy concerns and the complexity of integrating various data sources pose challenges, the overall market outlook remains highly positive, fueled by continuous technological advancements and growing digitalization across the APAC retail landscape. China and India, with their vast retail markets and rapidly evolving technological infrastructure, are expected to be the leading contributors to market expansion. The competitive landscape is dynamic, with a mix of established players like IBM, SAP, and Oracle, alongside specialized retail analytics vendors such as Qlik, Tableau, and Retail Next. These companies are focusing on innovation in areas such as predictive analytics, customer segmentation, and supply chain optimization to capture market share. Strategic partnerships, mergers and acquisitions, and the development of comprehensive, integrated platforms are becoming increasingly important competitive strategies. The success of companies in this space hinges on their ability to provide robust, user-friendly solutions that offer actionable insights and effectively address the specific needs of retailers across various segments and geographies. Future growth will likely be driven by the increased adoption of advanced analytics techniques, such as real-time analytics and sentiment analysis, and the integration of these analytics with other retail technologies, such as CRM and POS systems. This report provides a comprehensive analysis of the APAC Retail Analytics Market, covering the period 2019-2033. It delves into the market's size, growth drivers, challenges, and future trends, offering invaluable insights for businesses operating or planning to enter this dynamic sector. The study's base year is 2025, with estimations for 2025 and forecasts extending to 2033, utilizing historical data from 2019-2024. Key players like Qlik Technologies Inc, IBM Corporation, Adobe Systems Incorporated, SAP SE, and others are profiled. This report is essential for investors, retailers, and analytics providers seeking to navigate the complexities of this rapidly evolving market. Recent developments include: August 2022: Maxis invested in ComeBy, a Malaysia-based retail analytics startup, to bolster innovation and digitalization within the retail industry. ComeBy offers brick-and-mortar retailers valuable insights into individual shopper preferences before reaching the checkout counter. The company asserts that its approach, which combines both active and passive tracking, enhances customer engagement and optimizes in-store sales, as well as remarketing and merchandising efforts., June 2022: Amazon introduced a groundbreaking analytics tool that empowers consumer packaged goods (CPG) companies to monitor consumer interest in their products within Amazon Go and Amazon Fresh stores, known for their frictionless checkout technology. The new service, named Store Analytics, provides suppliers with "aggregated and anonymous insights" regarding customer interactions with their products, utilizing data collected by Amazon's innovative Walk Out and Dash Cart systems.. Key drivers for this market are: Increased Emphasis on Predictive Analysis, Sustained increase in volume of data; Growing demand for sales forecasting. Potential restraints include: Lack of general awareness and expertise in emerging regions, Standardization and Integration issues. Notable trends are: Solutions Segment is Anticipated to Hold Major Market Share.

This dataset encompasses mobile app based media consumption, collected from over 150,000 first-party US Daily Active Users on Android devices. Use it for measurement, journey understanding or to trigger surveys about sentiment. Platforms covered include Netflix, YouTube, Disney+ and Amazon Prime Video.

Fields include pre-roll ads played, viewing duration, channel, category and more. All data tied to demographics, all consumers can be surveyed about viewership (or other topics), and consumer journey understanding can be gleaned combining this dataset with other MFour OmniTraffic® products.