The Ultimate Guide to Sentiment Data 2021
What is Sentiment Data?
Sentiment data is a series of data points that come together to assess the general attitude of web users and potential customers towards a specific topic or brand. It’s used to create the most ideal audience to market to. As a company or business who is looking to market their goods or services in the optimum way, this data about public opinion and customer experiences whether positive, negative or neutral is important.
How is Sentiment Data collected?
The collection of information is important and when it comes to collecting it, this comes from web scraping tools which gather information from surveys, online APIs, databases and more. Every time a person goes online and works with a company, or leaves a review of the company, this information is then collected, added to the database and used.
What are the attributes of Sentiment Data?
The data that is collected is mostly feelings on working with companies, buying experiences, reviews, brand reputation, the web user’s name, age, demographics, and more. This information comes together to provide more insight on how people purchase and make decisions while online.
What is Sentiment Data used for?
Those companies and businesses who want to provide a better experience or increase and improve brand awareness. This data can be used to improve the customer experience delivered by the business you are running, so as to ensure that you’re maintaining a loyal customer base. Looking into consumers’ behaviors enables advertisers and marketers create ads and communication in line with online sentiment so that audiences respond to this outreach in the desired way.
How can a user assess the quality of Sentiment Data?
The quality of the data gathered can be checked based on whether the information given is through an actual person (user-supplied information) or if the information is from a bot or web scraping tool. User-supplied data is accurate because it reflects the sentiment of the user first-hand, although it’s rarely representative of a large audience. Web scraping tools, on the other hand, gather large volumes of sentiment data with broad geographical coverage. However, they’re only reliable if a sentiment data provider uses the correct algorithm to programm the bot. Checking whether a sentiment data proivder collects information from verified purchases and customers is one of the easiest ways to find out if the data collected is accurate.
Who are the best Sentiment Data providers?
Finding the right Sentiment Data provider for you really depends on your unique use case and data requirements, including budget and geographical coverage. Popular Sentiment Data providers that you might want to buy Sentiment Data from are Sentifi, Brain Company, Knowsis, RIWI, and The Data Appeal Company.
Where can I buy Sentiment Data?
Data providers and vendors listed on Datarade sell Sentiment Data products and samples. Popular Sentiment Data products and datasets available on our platform are Data Appeal’s Sentiment Score - analyze online sentiment customer perception with global sentiment data by The Data Appeal Company, Brain Sentiment Indicator - Stock Market Sentiment Data / Global Coverage / NLP-Sourced Data / Scores for 10,000+ Stocks by Brain Company, and SocialSentiment.io - Social media sentiment analysis of posts related to stocks - 30 days of history API by SocialSentiment.io.
How can I get Sentiment Data?
You can get Sentiment Data via a range of delivery methods - the right one for you depends on your use case. For example, historical Sentiment Data is usually available to download in bulk and delivered using an S3 bucket. On the other hand, if your use case is time-critical, you can buy real-time Sentiment Data APIs, feeds and streams to download the most up-to-date intelligence.
What are similar data types to Sentiment Data?
Sentiment Data is similar to Semantic Website Data, News Data, IP Address Data, Web Scraping Data, and Web Traffic Data. These data categories are commonly used for Sentiment Analysis and Sentiment Data analytics.