Best Sentiment Analysis Databases for Machine Learning and NLP projects
Sentiment analysis, a powerful tool in data analysis, helps businesses understand the emotions, opinions, and attitudes expressed in text data. To perform sentiment analysis effectively, reliable datasets are crucial. Find the best sentiment analysis datasets for your machine learning projects, their applications, and the best practices for sentiment analysis.
E-commerce Data | Product and Review Datasets | Global Coverage with Custom and On-demand Datasets | Price Monitoring and Brand Sentiment Analysis
Product Review Datasets for User Sentiment Analysis
Autoscraping | TripAdvisor Reviews | 8M+ Global Reviews with Sentiment Analysis
TagX Ecommerce Reviews data | Customer sentiment Analysis | Customer feedback data | Ecommerce data
LATAM & Mexico Consumer Sentiment Data | Consumer Trend Prediction & Analysis | 100% First Party Data
Related searches
Consumer Sentiment Data | Global Audience Insights | Psychographic Profiles & Trends | Best Price Guaranteed
AIKE| Web activity with brand and consumer sentiment analysis and shopping heat map|share of voice| brand health and transaction|Online&Offline Data
Consumer Marketing Data | Unique Consumer Sentiment Data: Transcription of the calls to the companies
Webautomation Reddit Data | Web Scraping Data | Uncover Trends, Sentiments and User Preferences | GDPR compliant
Dataplex: Google Reviews & Ratings Dataset | Track Consumer Sentiment & Location-Based Insights
What are sentiment analysis datasets?
Sentiment analysis datasets refer to collections of labeled text data that are used to train machine learning models to identify and classify sentiment. These datasets contain a wide range of textual content, such as customer reviews, social media posts, news articles, and more. By analyzing the sentiment expressed in this data, businesses can understand the overall emotional tone and gauge public opinion.
Best Sentiment Analysis Databases
| Rank | Provider Name | Dataset Name | Review |
|---|---|---|---|
| 1 | Grepsr | Sentiment Analysis of Facebook/Twitter/Instagram posts, News, Product Reviews - Custom and On-demand Sentiment Analysis- Grepsr | This dataset provides valuable insights into the sentiment of various social media platforms such as Facebook, Twitter, Instagram, and news articles. It can help businesses understand how the public perceives their company, products, and services. The custom and on-demand sentiment analysis feature allows for tailored analysis based on specific requirements. |
| 2 | TVEyes | Broadcast Transcript Feed with Sentiment Analysis (GBTS) | This dataset consists of broadcast TV transcripts enriched with sentiment analysis. It provides a comprehensive collection of broadcast metadata, sentiment scores, and company ticker symbols. It is a valuable resource for media analysis, allowing users to understand the sentiment expressed in television broadcasts and associated companies. |
| 3 | Data-Core | Sentiment analysis, opinion trends, and influencers in social media (Twitter / Facebook / Instagram / TikTok) | With this dataset, businesses can measure opinion trends and identify influencers on various social media platforms. The sentiment analysis uses Artificial Intelligence techniques to analyze the sentiment of user-generated content, providing insights into public opinion and trends related to different topics and products. |
| 4 | TagX | E-commerce Reviews dataset - analyse customer sentiment & competitors | This dataset contains customer reviews and ratings from popular e-commerce platforms like Amazon, Taobao, Tmall, Suning, and JD. It is a valuable resource for analyzing customer sentiment, understanding customer preferences, and gaining insights into product feedback. It also allows for competitor analysis, helping businesses identify strengths and weaknesses in the market. |
| 5 | Locationscloud | Locationscloud - Global Location, Business & Sentiment Data | This dataset combines geospatial data, internet sentiment, and market intelligence. It provides precise Point of Interest (POI) and location data along with business, financial, and sentiment assessments across 18 distinct industries. It is a comprehensive dataset for businesses looking to analyze global locations, understand market trends, and make informed decisions. |
| 6 | Grepsr | Aviation (Airlines) Datasets from Flight Aggregators - Grepsr | This dataset offers daily updates of aviation and travel data, including flight information, hotel websites, and vacation packages. It is particularly useful for airports, airlines, and travel technology companies, providing aviation/flight data, intelligence, and analytics for improved operations and decision-making. |
| 7 | Grepsr | Product and Review Datasets from Ecommerce websites - Grepsr | This dataset focuses on product and review data from e-commerce websites. It enables businesses to enhance their operations, make data-driven product decisions, identify areas for improvement, and improve the overall customer experience. It is a valuable resource for businesses operating in the e-commerce industry. |
| 8 | InfoTrie | InfoTrie’s Global Web Sentiment Data - Quantitative Analytical Platform | This dataset offers data intelligence and business intelligence solutions tailored to specific needs. It covers sentiment data for over 70,000 companies since 2013, using techniques such as Natural Language Processing (NLP), Named Entity Recognition (NER), and Machine Learning (ML). It provides attributes like date, timestamp, sentiment scores, volume, and buzz, allowing users to analyze sentiment and trends in web content. |
| 9 | ESG Analytics | Global ESG Material Events and Sentiment Data by ESG Analytics | This dataset focuses on Environmental, Social, and Governance (ESG) events mapped to companies globally. It provides sentiment analysis that indicates the magnitude of positive and negative sentiment. It is a valuable resource for investors and businesses interested in ESG factors and sustainability analysis. |
Understanding Sentiment Analysis
Sentiment analysis, also known as opinion mining, involves the use of natural language processing (NLP) techniques to determine the sentiment expressed in a piece of text. Whether it’s positive, negative, or neutral, sentiment analysis provides valuable insights into customer feedback, market trends, and public sentiment.
Extracting Actionable Insights
By utilizing sentiment analysis datasets, organizations can extract actionable insights from vast amounts of textual data. These insights can help businesses make data-driven decisions, improve customer experience, enhance brand reputation, and identify emerging trends.
Applications of Sentiment Analysis Datasets
Sentiment analysis datasets find applications across various industries, including:
1. Market Research and Competitive Analysis
Sentiment analysis enables businesses to understand consumer preferences, track competitors, and identify market trends. By analyzing sentiment across social media, product reviews, and forums, companies can gain a competitive edge.
2. Brand Reputation Management
Tracking sentiment allows organizations to monitor and manage their brand reputation effectively. By analyzing sentiment in customer reviews and online discussions, businesses can address negative sentiment promptly, identify areas for improvement, and leverage positive sentiment.
3. Customer Feedback Analysis
Sentiment analysis datasets help businesses gain insights into customer feedback and sentiment towards their products or services. This understanding allows organizations to address customer pain points, enhance customer satisfaction, and tailor their offerings to meet customer expectations.
Sentiment Analysis Datasets Use Cases
Social Media Monitoring
Companies leverage sentiment analysis datasets to monitor social media platforms and gain insights into public sentiment surrounding their brand, products, or industry. By tracking sentiment in real-time, businesses can identify emerging trends, respond to customer feedback promptly, and optimize their social media strategies.
Customer Support and Voice of Customer (VoC) Analysis
Sentiment analysis datasets enable businesses to analyze customer support interactions, including chat transcripts, emails, and support tickets. By analyzing the sentiment expressed in these interactions, companies can identify customer satisfaction levels, detect common issues, and improve their support processes.
Market Research and Product Development
Sentiment analysis datasets play a crucial role in market research and product development. Companies leverage these datasets to understand consumer preferences, assess the demand for new products or features, and gain insights into customer sentiment towards existing offerings.
Brand Monitoring and Crisis Management
Sentiment analysis datasets are instrumental in monitoring brand perception during a crisis or sensitive event. By analyzing sentiment across news articles, social media posts, and online forums, businesses can detect and address negative sentiment promptly, mitigate reputational risks, and effectively manage crisis situations.