In 2021, Google's share of online reviews increased to 71 percent, up from 67 percent in 2020, indicating a rise in willingness from consumers to share their experiences and opinions online. Overall, Google is the platform and search engine on which most consumers leave reviews for local businesses.

In 2021, many online shoppers in the United Kingdom (UK) considered what previous buyers had to say about products before purchasing the items themselves. Approximately **** in *** UK consumers stated they would check online reviews before buying from a particular business. Even more shoppers said they often avoid enterprises with a rating lower than four.

In recent years, it has become increasingly important to the consumer to read up on a product, business, or service before spending any money. In 2021, nearly ** percent of online shoppers typically read between *** and *** customer reviews before making a purchasing decision. Less than *** in *** shoppers did not have a habit of reading customer reviews before buying.

9 variables including review comments scrapped from a leading online retail portal . Include reviews from 3600 customers

In 2022, almost *** in *** consumers in the United States reported always reading ratings and reviews when they shopped online for clothing. In contrast, only ***** percent of survey respondents reported doing so on an occasional basis, indicating that ratings and reviews are an important purchase criterion for online apparel shoppers.

The dataset is from a B2C e-commerce platform in China, with massive product negative reviews of four representative sectors including Computers, Phone&Accessories, Gifts&Flowers and Clothing.Here the negative reviews are defined as the reviews with scores 1. After the raw data was collected, deduplication, user anonymization & categorization and text classification was employed to process the raw data. The data contains fields of id for comment, anonymous id for user, review text, timestamp of the posting, negative reason label and user level.

The dataset contains four JSON files, with each file titled by the corresponding sector name.In each JSON file, each line represents a record of a negative review from this sector, in which the filed ‘id’ is the unique code we created for reviews, the filed ‘userID’ is the unique code we created for users, the field ‘userLevel’ is the user’s level in the platform, the field ‘creationTime’ is the timestamp a review was posted, the filed ‘content’ is the review text in Chinese and the field ‘label’ represent why the consumers post the negative reviews, in which 0 for Logistic, 1 for Product function, 2 for Consumer Service and 3 for False Marketing.

The dataset comes from our paper:

Sun M, Zhao J. Behavioral Patterns beyond Posting Negative Reviews Online: An Empirical View. Journal of Theoretical and Applied Electronic Commerce Research. 2022; 17(3):949-983. https://doi.org/10.3390/jtaer17030049

If it is helpful, please cite the paper.

This work was supported by NSFC (Grant No. 71871006).

This dataset contains 2 sets of data files that was used in studying genderbias in the evaluation and use of consumer online reviews. AmazonData.csv is data extracted from the Amazon site. YelpData.csv is data from the Yelp site.

In 2023, more than *** in *** consumers from the United States reported that they always read reviews when shopping for beauty products online. Additionally, **** percent reported that they sometimes consult online reviews.

Access our Trustpilot Reviews Data in CSV Format, offering a comprehensive collection of customer reviews from Trustpilot.

This dataset includes detailed reviews, ratings, and feedback across various industries and businesses. Available in a convenient CSV format, it is ideal for market research, sentiment analysis, and competitive benchmarking.

Leverage this data to gain insights into customer satisfaction, identify trends, and enhance your business strategies. Whether you're analyzing consumer sentiment or conducting competitive analysis, this dataset provides valuable information to support your needs.

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.

Context

This Dataset is an updated version of the Amazon review dataset released in 2014. As in the previous version, this dataset includes reviews (ratings, text, helpfulness votes), product metadata (descriptions, category information, price, brand, and image features), and links (also viewed/also bought graphs). In addition, this version provides the following features:

• More reviews:

  • The total number of reviews is 233.1 million (142.8 million in 2014).

• New reviews:

  • Current data includes reviews in the range May 1996 - Oct 2018.

• Metadata: - We have added transaction metadata for each review shown on the review page.

  • Added more detailed metadata of the product landing page.

Acknowledgements

If you publish articles based on this dataset, please cite the following paper:

• Jianmo Ni, Jiacheng Li, Julian McAuley. Justifying recommendations using distantly-labeled reviews and fined-grained aspects. EMNLP, 2019.

Utilize our Amazon reviews dataset for diverse applications to enrich business strategies and market insights. Analyzing this dataset can aid in understanding customer behavior, product performance, and market trends, empowering organizations to refine their product and marketing strategies. Access the entire dataset or tailor a subset to fit your requirements. Popular use cases include: Product Performance Analysis: Analyze Amazon reviews to assess product performance, uncovering customer satisfaction levels, common issues, and highly praised features to inform product improvements and marketing messages. Customer Behavior Insights: Gain insights into customer behavior, purchasing patterns, and preferences, enabling more personalized marketing and product recommendations. Demand Forecasting: Leverage Amazon reviews to predict future product demand by analyzing historical review data and identifying trends, helping to optimize inventory management and sales strategies. Accessing and analyzing the Amazon reviews dataset supports market strategy optimization by leveraging insights to analyze key market trends and customer preferences, enhancing overall business decision-making.

You can analyze the Yelp's data the OpenWeb Ninja API provides to gain insights into the business world. This includes looking at market trends, identifying popular business categories, reading customer reviews and ratings, and understanding the factors that contribute to business success or failure.

The dataset includes all key business listings data & consumer review data:

Business Type, Description, Categories, Location, Consumer Review Data, Review Rating, Review Reactions, Review Author Information, Licenses, Highlights, and more!

According to a March 2019 survey of shoppers in the United States, 68 percent of respondents stated that they paid attention to star ratings when judging a brand or retailer. A further 61 percent of respondents stated that they also regarded the quantity of reviews as important.

Abstract: With the e-commerce growth, more people are buying products over the internet. To increase customer satisfaction, merchants provide spaces for product and service reviews. Products with positive reviews attract customers, while products with negative reviews lose customers. Following this idea, some individuals and corporations write fake reviews to promote their products and services or defame their competitors. The difficulty for finding these reviews was in the large amount of information available. One solution is to use data mining techniques and tools, such as the classification function. Exploring this situation, the present work evaluates classification techniques to identify fake reviews about products and services on the Internet. The research also presents a literature systematic review on fake reviews. The research used 8 classification algorithms. The algorithms were trained and tested with a hotels database. The CONCENSO algorithm presented the best result, with 88% in the precision indicator. After the first test, the algorithms classified reviews on another hotels database. To compare the results of this new classification, the Review Skeptic algorithm was used. The SVM and GLMNET algorithms presented the highest convergence with the Review Skeptic algorithm, classifying 83% of reviews with the same result. The research contributes by demonstrating the algorithms ability to understand consumers’ real reviews to products and services on the Internet. Another contribution is to be the pioneer in the investigation of fake reviews in Brazil and in production engineering.

Product Reviews Software Market size was valued at USD 8.7 Billion in 2024 and is projected to reach USD 28.9 Billion by 2031, growing at a CAGR of 14.7 % during the forecasted period 2024 to 2031

Global Product Reviews Software Market Drivers

The market drivers for the Product Reviews Software Market can be influenced by various factors. These may include:

Growing Significance of Customer Feedback: As online shopping and e-commerce have grown in popularity, customers now heavily consider customer feedback when making selections about what to buy. Product reviews software helps companies gain credibility and confidence from prospective customers by efficiently gathering, organizing, and presenting consumer feedback.

Put Customer Experience (CX) first: Creating a satisfying consumer experience is a top concern for companies in all sectors. With the use of product reviews software, businesses can get client feedback in real time, pinpoint areas for development, and quickly resolve issues, all of which increase customer happiness and loyalty.

Impact on Purchase Behavior: Research indicates that most consumers base their decisions on what to buy on product reviews. Negative reviews might discourage potential consumers, while positive ratings can greatly impact purchasing behavior and increase sales. Businesses can employ user-generated content to boost conversion rates and spur revenue growth by utilizing product reviews software.

Benefits of SEO: Product reviews and other user-generated content are essential to search engine optimization (SEO). Product reviews software can raise a business's search engine rankings, increase organic traffic to its website, and improve online exposure by producing new and pertinent content. These actions will eventually increase sales and brand awareness.

Enhanced Product Insights: Software for product reviews offers insightful data on consumer preferences, problems, and product effectiveness. Businesses can enhance their product offerings and marketing strategies by identifying patterns, evaluating the strengths and weaknesses of their products, and making data-driven decisions by assessing review data and sentiment.

Social Proof and Trust Building: Positive product reviews act as social proof of a product's dependability, worth, and quality. This promotes trust in the brand. Businesses may differentiate themselves from rivals, gain the trust of prospective customers, and establish a solid reputation for their brands in the marketplace by displaying real client feedback.

Competitive Advantage: Companies can maintain their competitiveness in today's congested markets by putting product reviews software into place. Businesses can set themselves apart from competitors who might not have as strong of a review management strategy, foster brand loyalty, and differentiate their products by aggressively controlling and promoting user reviews.

Brand Engagement and Community Building: Software for product reviews encourages communication and engagement between companies and their clients. Companies may create a feeling of community around their products and brand, develop brand champions, and forge deep connections with their audience by replying to reviews, answering customer questions, and requesting feedback.

Continuous Improvement: Product reviews offer insightful information for new and improved products. Businesses may better satisfy consumer wants and expectations by identifying areas for improvement, iterating on product features, and continuously evolving their offers by listening to customer input.

Context

Welcome. This is a Women’s Clothing E-Commerce dataset revolving around the reviews written by customers. Its nine supportive features offer a great environment to parse out the text through its multiple dimensions. Because this is real commercial data, it has been anonymized, and references to the company in the review text and body have been replaced with “retailer”.

Content

This dataset includes 23486 rows and 10 feature variables. Each row corresponds to a customer review, and includes the variables:

• Clothing ID: Integer Categorical variable that refers to the specific piece being reviewed.
• Age: Positive Integer variable of the reviewers age.
• Title: String variable for the title of the review.
• Review Text: String variable for the review body.
• Rating: Positive Ordinal Integer variable for the product score granted by the customer from 1 Worst, to 5 Best.
• Recommended IND: Binary variable stating where the customer recommends the product where 1 is recommended, 0 is not recommended.
• Positive Feedback Count: Positive Integer documenting the number of other customers who found this review positive.
• Division Name: Categorical name of the product high level division.
• Department Name: Categorical name of the product department name.
• Class Name: Categorical name of the product class name.

Acknowledgements

Anonymous but real source

Inspiration

I look forward to come quality NLP! There is also some great opportunities for feature engineering, and multivariate analysis.

Publications

Statistical Analysis on E-Commerce Reviews, with Sentiment Classification using Bidirectional Recurrent Neural Network
by Abien Fred Agarap - Github

Gain valuable customer feedback for specific locations with daily updated Google Reviews & Ratings data. Perfect for businesses looking to analyze sentiment, track performance, and benchmark against competitors.

1) Data Introduction • The Samsung Customer Reviews Dataset contains 1,000 customer reviews of various Samsung products, including smartphones, tablets, TVs, and smartwatches. User feedback, ratings, and timestamps are included, which are useful for emotional analysis, customer satisfaction surveys, and product quality assessment.

2) Data Utilization (1) Samsung Customer Reviews Dataset has characteristics that: • This dataset contains structured text and numerical information for each review, including product name, username, rating, review title, review body, and creation date, for detailed analysis by review. (2) Samsung Customer Reviews Dataset can be used to: • Customer Opinion Analysis and Emotional Classification: Review texts and ratings can be used to identify customer positive and negative emotions, major complaints and compliments about Samsung products, and to improve products and develop marketing strategies. • Comparison of satisfaction and trend analysis by product: By analyzing review data by product group and period, market trends such as popular products, changes in customer preferences, and repeatedly mentioned issues can be derived and used for competitor analysis or new product planning.

This is dataset for customer online reviews of upper limb rehabilitation devices.