Today, the internet is a maze of opinions. Buyers look to these opinions for an insight into which products to purchase or avoid. Given consumer feedback has a lot of power, the only way to stay ahead of the competition is to get a clear understanding of what customers are trying to say in the many texts shared online.
And the best way to understand consumer emotion and opinion is through sentiment analysis. Businesses across all sectors should consider data scraping for sentiment analysis to get valuable insights from customer feedback.
Sentiment analysis by definition
Also known as opinion mining, sentiment analysis is a natural language processing method that identifies or makes sense of emotions expressed in a text. Sentiment analysis entails mining text for sentiments and biased information using artificial intelligence, data mining, and machine learning.
In most cases, organizations utilize AI sentiment analysis tools to gauge opinions from an unorganized and unstructured block of text gathered from online sources such as social media, web forums, blog post comments, and emails.
Sentiment analysis systems automate collecting and analyzing insights from feedback by replacing manual data processing with algorithms that implement automatic, rule-based, and hybrid techniques.
Implementing rule-based systems enables organizations to perform sentiment analysis based on pre-defined, lexicon-set rules. Automatic systems gather insights from data using machine learning techniques, while hybrid systems combine both methods.
Types of sentiment analysis
1. Fine-grained sentiment analysis
This type of analysis guarantees an in-depth understanding of your customers’ sentiments by breaking reviews and ratings into various categories. By using fine-grained analysis, organizations get precise polarity or level of positivity and negativity within a text.
This technique is popular among e-commerce site owners as it helps them understand consumer sentiments.
2. Intent-based analysis
The intent analysis provides a deeper understanding of customers’ intentions by identifying emotions behind a text within a comment or review. For example, a customer’s online comment expressing frustration about using a washing machine could prompt customer service of the specific brand to reach out and solve the problem.
3. Emotion detection
As the name suggests, this analysis helps identify and understand people’s emotions like happiness, anger, anxiety, fear, and sadness. To detect emotions in a block of text, organizations must use machine learning and lexicons (collection of positive and negative words) to separate terms as per their actual sentiments.
4. Aspect-based analysis
This analysis focuses on a specific aspect of a service or product to help companies understand how consumers feel about a particular product’s features. For example, if you sell smartphones, you can use aspect-based analysis to determine if customers like or dislike the camera or battery life.
Benefits of sentiment analysis
1. Real-time analysis
Considering sentiment analysis is powered by machine learning, it can detect problems in real-time. This means businesses can identify problems immediately they arise and take action to solve them before a public relations crisis escalates on social media.
2. Monitor brand performance
A brand is a system that sparks different emotions in a consumer that can make or break your company’s success. That’s why it’s crucial to use sentiment analysis to monitor your brand’s performance.
Analyzing customer feedback or product reviews using sentiment analysis can help determine consumers’ positive and negative emotions towards your brand. Depending on the outcomes, you can develop a plan to improve your products to meet customer needs.
3. A reliable source for consumer feedback
Every successful business values what consumers think about their services or products. However, sorting through thousands of Facebook comments, tweets, surveys, and customer support chats is daunting.
That’s where sentiment analysis comes in, as it helps gather customer sentiments expressed publicly by mining opinions on social media, discussion forums, review pages, and blog posts. With the help of artificial intelligence, natural language processing, and machine learning technologies, access to general consumer feedback is attainable.
Google scraping for sentiment analysis
Analyzing reviews, customer feedback, and social media comments manually to get insights into customer sentiments is daunting. Fortunately, there is an efficient way to maximize sentiment analysis and achieve the desired results.
To gather information for sentiment analysis, you’ll want to invest in data scraping. Data scraping is an automated process of collecting large amounts of information about a specific subject from various online sources.
Scraping for sentiment analysis could involve using a custom-built scraper or third-party data scraping tools. Third-party scraping tools provide data in a structured HTML or JSON format, which you can use to analyze customer emotion and opinion.
When using a data scraper, instruct it to search for the information you need. For example, let’s say you want comments on every phone brand. A scraper will collect feedback and arrange it into a file.
To learn more about scraping for sentiment analysis, check out this blog post by SERPMaster.
Conclusion
Consumer emotion and opinion play a significant role in the success of any business. A single opinion shared online can encourage or drive away customers from doing business with a brand. For this reason, all businesses should commit time and resources to sentiment analysis.
Scraping data for sentiment analysis using the methods highlighted above eliminates the guesswork in determining how your customers feel about your brand.
