BDFoodReview is a large-scale dataset containing 334,119 restaurant reviews collected from "Foodpanda Bangladesh". The dataset includes customer reviews in mixed languages (Bangla, English, and Banglish), translated into English, along with their corresponding ratings and sentiment labels.

Dataset Statistics Total Reviews: 334,119 Features/Columns: 19

Potential Applications Sentiment Analysis Restaurant Review Classification Customer Satisfaction Analysis Opinion Mining Natural Language Processing Research Food Service Industry Analysis

The Yelp Restaurant Reviews Dataset (Sample Extract) comprises 14,351 individual restaurant review records collected from the official Yelp Open Dataset, designed to support research in sentiment analysis, natural language processing, and customer behaviour analytics. Each record corresponds to a single customer review and integrates mixed data types, including textual content (full review text), numerical attributes (review star rating on a 1–5 scale, overall business rating, and total review count), and categorical metadata (business name, restaurant categories, city, state, and sentiment label). The dataset spans multiple U.S. cities and states, covering the restaurant and food service domain, and includes a pre-labelled sentiment variable (Positive, Neutral, Negative) to facilitate supervised learning tasks. Temporal information is preserved through review timestamps, enabling time-based analysis, while business-level aggregation variables support recommendation systems and reputation modelling studies. The dataset is provided in CSV format, ensuring compatibility with common data science tools and workflows.

The file contains four public datasets, which are taken from user comments on Laptop and Restaurant. Each dataset is composed of review, aspect words composed of one or more words, and the sentiment polarity of the aspect word. The sentiment polarity is composed of Positive, Neutral, and Negative.

Review: This column contains textual reviews provided by customers. Each row represents a specific customer's opinion or feedback about their experience at the restaurant. The reviews may include positive or negative comments about various aspects such as food, service, ambiance, cleanliness, etc.

Liked: This column represents the rating given by customers to indicate whether they liked or disliked their experience. The values in this column are numerical, with 1.0 indicating that the customer liked the restaurant and 0.0 indicating that the customer did not like it. The "Liked" column serves as the target variable in a binary classification task, where the goal could be to predict whether future customers will like or dislike the restaurant based on their reviews.

The dataset contains 2221 rows and 2 columns

2220 rows × 2 columns

Overview This dataset contains 1,000 text reviews from various restaurants, with each review labeled as either positive or negative. It is designed for beginners in the field of sentiment analysis and natural language processing (NLP).

Column Description - Unnamed: 0: An index or identifier for each review (can be ignored for analysis). - sentence: The actual text of the restaurant review. - label: The sentiment of the review: -1: Positive review -0: Negative review

Usage This dataset is a begginer friendly can be used to train and evaluate sentiment analysis models. It is ideal for binary classification tasks and is suitable for educational purposes, such as learning text preprocessing, feature extraction, and classification algorithms.

The dataset used in Aspect Term Extraction Using Deep Learning-Based Approach on Indonesian Restaurant Reviews paper, by Rachmansyah Adhi Widhianto and Ade Romadhony.

This dataset contains 9,551 restaurant records from 15 different countries with details on cuisines, pricing, table booking availability, online delivery, user ratings, and more. It can be useful for food business analysis, recommendation systems, price prediction, and sentiment analysis.

💡 Key Highlights: ✅ Covers 15 countries (India, USA, UK, UAE, Australia, etc.) ✅ Over 7,400 unique restaurants across 141 cities ✅ Includes cuisines, average cost, and rating details ✅ Helpful for business insights, trend analysis, and ML projects.

Restaurant Reviews CZ ABSA - 2.15k reviews with their related target and category

The work done is described in the paper: https://doi.org/10.13053/CyS-20-3-2469

This dataset corresponds to the paper "'Authentic and amazing': authenticity as an evaluative category in online consumer restaurant reviews" appearing in Cultural Analytics. This dataset provides the R scripts used for the preparation, analysis as well as the import of data to Sketch Engine, the ID lists of the reviews in Corpus 1, 2 and 3, as well as the authenticity lexicons used which were derived from O'Connor et. al (2017) under a CC BY 4.0 license. The IDs correspond the those in the Yelp Dataset at the time of data collection (2019).

This dataset contains all restaurant reviews for Perlis, Malaysia, along with sentiment analysis scores. The dataset includes details such as review text, ratings, timestamps, reviewer information, and sentiment labels (positive, negative, neutral) with corresponding sentiment scores. This dataset is useful for analyzing customer feedback, sentiment trends, and restaurant performance.

This file contains three publicly available datasets, with data sourced from user reviews of laptops, restaurants, and Twitter posts. Each piece of data consists of a review text, one or more aspect terms (such as "screen" and "service"), and their corresponding sentiment polarities (Positive/Negative/Neutral). It is suitable for aspect-level sentiment analysis tasks.

Dataset

This dataset was created by Abhijeet kumar128

Released under CC0: Public Domain

Contents

There are many contexts where dyadic data are present. In social networks, users are linked to a variety of items, defining interactions. In the social platform of TripAdvisor, users are linked to restaurants by means of reviews posted by them. Using the information of these interactions, we can get valuable insights for forecasting, proposing tasks related to recommender systems, sentiment analysis, text-based personalisation or text summarisation, among others. Furthermore, in the context of TripAdvisor there is a scarcity of public datasets and lack of well-known benchmarks for model assessment. We present six new TripAdvisor datasets from the restaurants of six different cities: London, New York, New Delhi, Paris, Barcelona and Madrid. If you use this data, please cite the following paper under submission process (preprint - arXiv) We exclusively collected the reviews written in English from the restaurants of each city. The tabular data is comprised of a set of six different CSV files, containing numerical, categorical and text features: parse_count: numerical (integer), corresponding number of extracted review by the web scraper (auto-incremental) author_id: categorical (string), univocal, incremental and anonymous identifier of the user (UID_XXXXXXXXXX) restaurant_name: categorical (string), name of the restaurant matching the review rating_review: numerical (integer), review score in the range 1-5 sample: categorical (string), indicating “positive” sample for scores 4-5 and “negative” for scores 1-3 review_id: categorical (string), univocal and internal identifier of the review (review_XXXXXXXXX) title_review: text, review title review_preview: text, preview of the review, truncated in the website when the text is very long review_full: text, complete review date: timestamp, publication date of the review in the format (day, month, year) city: categorical (string), city of the restaurant which the review was written for url_restaurant: text, restaurant url

This comprehensive dataset consists of authentic restaurant reviews, providing valuable insights into customer sentiments. Each entry includes a textual comment from the customer ('review') and a binary label ('liked') indicating whether the customer enjoyed the restaurant experience (1) or not (0). The dataset serves as a valuable resource for sentiment analysis and machine learning tasks related to restaurant reviews.

The Consumer Ratings & Reviews Software market is booming, projected to reach $478 million by 2025 with a 12.7% CAGR. Discover key trends, leading companies, and regional insights in this comprehensive market analysis covering cloud-based & on-premise solutions across various industries.

The restaurant test data for Subtask 1 Phase A evaluation of the SemEval 2016 Task 5: Aspect Based Sentiment Analysis (ABSA) for French (120 reviews, 668 sentences).

Use cases that can be supported with Yelp Reviews

A. Market Research and Analysis: Leverage Yelp data to conduct comprehensive market research and analysis in the restaurant industry. Identify emerging culinary trends, popular cuisines, and customer preferences. Gain a competitive edge by understanding your target audience's needs and expectations.

B. Competitor Analysis: Compare and contrast your restaurant with competitors on Yelp. Analyze their ratings, customer reviews, and performance metrics to identify strengths and weaknesses. Use these insights to enhance your offerings and stand out in the market.

C. Reputation Management: Monitor and manage your restaurant's online reputation effectively. Track and analyze customer reviews and ratings on Yelp to identify improvement areas and promptly address negative feedback. Positive reviews can be leveraged for marketing and branding purposes.

D. Pricing and Revenue Optimization: Leverage the Yelp dataset to analyze pricing strategies and revenue trends in the restaurant sector. Understand seasonal demand fluctuations, pricing patterns, and revenue optimization opportunities to maximize your restaurant's profitability.

E. Customer Sentiment Analysis: Conduct sentiment analysis on Yelp reviews to gauge customer satisfaction and sentiment towards your restaurant. Use this information to improve dining experiences, address pain points, and enhance overall customer satisfaction.

F. Content Marketing and SEO: Create compelling content for your restaurant's website based on popular keywords, cuisines, and dining preferences identified in the Yelp dataset. Optimize your content to improve search engine rankings and attract more potential diners.

G. Personalized Marketing Campaigns: Use Yelp data to segment your target audience based on dining preferences, food habits, and demographics. Develop personalized marketing campaigns that resonate with different customer segments, resulting in higher engagement and repeat business.

H. Investment and Expansion Decisions: Access historical and real-time data on restaurant performance and market dynamics from Yelp. Utilize this information to make data-driven investment decisions, identify potential areas for expansion, and assess the feasibility of new culinary ventures.

I. Predictive Analytics: Utilize the Yelp dataset to build predictive models that forecast future trends in the restaurant industry. Anticipate shifts in culinary preferences, understand customer behavior, and make proactive decisions to stay ahead of the competition.

J. Business Intelligence Dashboards: Create interactive and insightful dashboards that visualize key performance metrics from the Yelp dataset. These dashboards can help restaurant executives and stakeholders get a quick overview of the restaurant's performance and make data-driven decisions.

Incorporating the Yelp dataset into your business processes will enhance your understanding of the restaurant market, facilitate data-driven decision-making, and provide valuable insights to drive success in the competitive culinary industry.

Turkish Sentiment Dataset (Restaurant Reviews)

This dataset contains 15 manually created Turkish sentences labeled for sentiment analysis. The sentences are related to restaurant experiences and are categorized as positive, negative, or neutral.

  Labels

olumlu (positive) olumsuz (negative) nötr (neutral)

  Format

The dataset uses $ as a delimiter to avoid issues with commas inside the text.

  Example

Yemeklerin lezzeti ve sunumu tam bir şölendi, şefi… See the full description on the dataset page: https://huggingface.co/datasets/hanerdem/turkish-sentiment-dataset.

Context

This is a dataset for practicing Sentiment Analysis, Text analytics and classification etc. Dataset will be updated on regular basis by scrapping reviews from websites .

Content

Analyse your NLP skills and make some amazing notebooks and perform some text classification . The reviews can be positive or negative/liked or disliked . Positive Review : 1 Negative Review : 0

Sentiment Aware Review Summarization Dataset

  Dataset Summary

This dataset is designed for Aspect-Based Sentiment Analysis (ABSA) and Review Summarization tasks. It contains restaurant reviews paired with structured sentiment annotations (categorized by aspects) and a concise text summary. It is ideal for training Large Language Models (LLMs) to perform multi-task operations: analyzing specific aspects of a customer experience while simultaneously generating a brief… See the full description on the dataset page: https://huggingface.co/datasets/navdeep-singh/sentiment-aware-review-summarization.