Today, we see an increasing number of companies incorporating sentiment analysis into business processes — from market research and product innovation to brand monitoring and customer experience management. Sentiment analysis involves analyzing large volumes of data to gauge emotional responses from customers or the public. The challenge lies in obtaining enough relevant and timely data to understand how customers feel about products and brands.
How companies get data for customer sentiment analysis
You can find many sources of relevant data for sentiment analysis:
- Social media platforms — E.g., Facebook, LinkedIn, WeChat, Reddit.
- Sites on the open web — Includes news sites, blogs, forums, and review sites.
- Internal systems and apps — E.g., customer relationship management software (CRMs), email systems, chatbot histories.
- Customer outreach — Includes online or emailed customer surveys, dedicated feedback forms, or customer interviews.
While you can get relevant information from internal systems and customer outreach, the open web contains a wealth of information about consumers. You can gain deep insights into customers by using web data for sentiment analysis.
The best method to collect web data depends on the source, and methods include:
- Ad-hoc web scraping — You create a list of websites you’d like scraped, and the data provider will store the scraped data in a database or structured file. Some vendors will deliver the data via an API. Most ad-hoc web scraping solutions require that you have developers manage lists of crawled websites and maintain the scraping tools. In general, ad-hoc web scraping solutions are designed for small-scale data projects.
- Traditional APIs — You use an API that returns data collected from various sources and stored in a database or text files (typically converted to JSON). Some APIs will provide access to in-memory databases for real-time data retrieval. An API solution doesn’t require developers to maintain lists, but you do need developers to integrate the API with your platform or application. APIs work well for use cases where you need high scalability and speed.
- Ad-hoc APIs — You use an API that returns continuous data feeds based on ad-hoc queries. For example, our eCommerce Reviews API provides structured reviews data in the form of a web data feed. Each feed is based on an ad-hoc, product-specific query created by the user. You can use queries to refine the data feeds returned by the API, allowing you to get the precise data you need for your use case.
- Manual collection — For most data sources, you would use an ad-hoc web scraping solution or a web data feed API. However, many companies collect data from customer outreach methods manually, entering that data into spreadsheets or databases. You need to have adequate human resources for this type of data collection.
Our Web Data 101 guide highlights the differences between ad-hoc web scraping and APIs that provide web data feeds.
Challenges in getting data for customer sentiment analysis
Many sentiment analysis tools and platforms provide users with insights or reports based on the data ingested (with some help from machine learning). Some companies have started enhancing sentiment analysis solutions with AI. Regardless of the techniques used, sentiment analysis tools need quality, relevant data to work well.
The ways that companies get data — web scraping, APIs, manual collection — have their own set of challenges, which can include:
- Lack of comprehensiveness — Web scraped datasets usually provide a limited amount of data, so you don’t get enough data for a complete picture of consumer sentiment.
- Poor quality — Generally, web-scraped data and public datasets (like Common Crawl) contain a lot of noise and unwanted content. These things can get in the way of a proper analysis of the data.
- High cost — If you use data with unwanted content and noise, you must invest in more human resources to clean and preprocess the data.
- Time-consuming — Manual data collection methods take a lot of time and can’t keep up with rapidly changing consumer sentiment. Cleaning and preprocessing scraped web data also takes a lot of time.
- Bias — Some APIs and data sets provide a limited or unrepresentative sample of data, which can lead to bias when used to train a model or to generate insights.
- Not scalable — Data collection methods involving web scraping or manual processes are difficult and costly to scale because of the increasing number of people needed to do so.
- Not multilingual — Most data sources come in one language, usually English. Customer sentiment comes in many languages, and sometimes sentiment can get lost in translation.
You can avoid these problems if you use an API that returns structured web data feeds.
Get a fuller picture with Webz.io’s eCommerce Reviews API
To get a fuller picture of consumer sentiment, you should incorporate product reviews data with your platform or application. With the Webz.io eCommerce Reviews API, you can help your customers discover how consumers feel about their products, and ultimately, their brands. The Webz.io eCommerce Reviews API gives you:
Comprehensive data collection
The eCommerce Reviews API provides product and reviews data from 900+ eCommerce sources, including marketplaces and consumer packaged goods (CPG). Supported eCommerce and marketplace data sources include Amazon, Target, Walmart, and Wayfair. You also get data from growing categories such as travel and hospitality, restaurants, and job sites. You can see a complete list of supported data sources here.
With access to reviews data at scale, you can expand your data coverage so that sentiment analysis results are more representative and less biased. You also get reviews in multiple languages, so you can train your models to understand sentiment in more than one language. You can use our reviews data to obtain a fuller picture of customer sentiment.
High-quality data on demand
The API provides structured product-specific reviews data, allowing users to get critical, unbiased information about customer sentiment. You can search for products by URL, keyword, or category, getting all the product and reviews data you need. By consuming high-quality data on an ad-hoc basis, users can obtain consumer sentiments for specific products and gain deep insights into their customers at any time and in near real-time. Plus, the data returned by the API is already cleaned, processed, and enriched, saving you a lot of preprocessing time.
A cost-effective and scalable solution
We’ve created a ready-made, scalable solution to getting more high-quality data — you only have to integrate our API with your sentiment analysis platform or application. And we’ve already collected over one million structured product data and the one billion reviews that go with them.
Boost your sentiment analysis models
You have many choices regarding where and how to get data, and the method you choose directly impacts the results of your sentiment analysis models. With the Webz.io eCommerce Reviews API, you can boost sentiment analysis by incorporating relevant data from massive streams of product reviews. More relevant data means you get a fuller picture of customer sentiment.
Interested in learning more about the eCommerce Reviews API? Talk to one of our data experts.
