Anyone who works for a software company is probably all too familiar with social media. Not only do the relevant computer technologies take up most of your business model, but your customers are rarely hesitant to use social media platforms to share their opinions of your products. Of course, it’s nice when those opinions are positive. Still, the sheer number of text-based opinions on the internet can be frustrating for anyone trying to gauge how their customers are feeling accurately.
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Many companies turn to web-scraping programs for a clear picture of their customers’ satisfaction or dissatisfaction as expressed online. But traditional language processing technologies, while beneficial to a certain extent, are often limited in how well they can accurately evaluate more complex opinions found in online texts. Fortunately for you, “opinion mining” offers a new and exciting means of assessing opinions in real-time as they appear on social media sites.
Read on to learn more about opinion mining, or use the table of contents to skip ahead to the section most relevant to you.
What is Opinion Mining?
Opinion mining is a novel type of textual analysis that many businesses and marketing firms now use to understand the specific opinions of a product or service among a broad customer base. Like previous types of textual analysis, opinion mining employs state-of-the-art natural language processing techniques borne from artificial intelligence technology to derive data from large quantities of online texts. But opinion mining goes beyond traditional data limitations and allows companies to get a clearer picture of customer opinions in a more comprehensive and nuanced way.
Opinion mining arose from the more familiar textual analysis known as “sentiment analysis.” Indeed, many companies that advertise this service use the two terms interchangeably. But opinion mining differs in a few crucial ways from more traditional sentiment analysis.
Whereas older sentiment analysis techniques are limited to surface-level readings of emotional expressions in given texts, opinion mining can provide companies with more comprehensive pictures of complete opinions held by customers. In other words, opinion mining can (as its name suggests) provide much more information on customers’ opinions. This includes the “what” and “why” of the opinion instead of merely the general emotions a customer expresses in a given text.
To put it another way, traditional sentiment analysis provides you with information on how a customer feels about your product or service. Opinion mining, on the other hand, can help you better understand why they feel that way. It does this using deeper and more advanced natural language processing technology that can better decipher and analyze texts that express opinions.
The genius of opinion mining for a Software as a Service company is that it allows you to get a clearer picture of how customers feel about your product or service using real-time social media posts. With social media increasingly becoming a major means of expressing opinions on recent purchases, companies have an excellent range of useful data at their fingertips if they can utilize technology that can read and process actual opinion content.
Instead of wasting time, money, and resources on customer feedback surveys, focus groups, and other traditional methods of data collection, you can use opinion mining to follow along with real-time internet and social media reactions to your product. With technology that can not only tell you what customers feel about your product or service but why they feel that way, you can see what needs to be changed, why it needs to be changed, and in what direction it should change. And you get all of this at a much quicker and more efficient pace.
The more advanced language processing capabilities of opinion mining can answer several vital questions necessary for designing and promoting services in the future. For example, you can quickly learn what your customers want when buying your product or service, what they need to get to feel satisfied, what they need to get to recommend your product to others, and, if they are dissatisfied with your product, what precisely you need to improve.
Different Types of Opinion Mining
Today, opinion mining comes in several different types, each offering specific resources that are useful in other circumstances. If you decide to use opinion mining services for your business, it’s a good idea to investigate the main types of opinion mining, how they work, and what kind of data they can provide. That way, you can be sure that you use the best possible tools for your business and marketing needs. Here are four of the most notable types of opinion analysis and what benefits they can provide to your business.
Fine-grained sentiment analysis
Fine-grained sentiment analysis is a type of opinion mining that categorizes opinions based upon a continuum, or scale, according to different extremes. In most cases, this scale classifies opinions ranging from “very positive” to “very negative.” With modern language processing technology, fine-grained sentiment analysis mining can identify nuance between degrees of opinion. For example, it can tell you the difference between a customer opinion that is “very positive” from one that is “moderately positive” or leaning toward neutral. This type of opinion mining is helpful because it can provide categorical data from open-ended survey questions and general social media posts.
As the name suggests, “emotion detection” is a type of opinion mining that identifies specific emotions within larger texts. Using this type of opinion mining, you can get a clearer picture of the specific emotions that your customers feel when using your product or service. You can learn, for instance, whether they are happy, angry, irritated, relieved, disappointed, and so on. Advanced textual analysis has given emotion detection technology a greater ability to decipher more difficult figurative language (such as sarcasm or hyperbole) to accurately assess the emotions within.
Aspect-based sentiment analysis
This beneficial type of opinion mining can provide data on opinions that customers express on different aspects of your product or service. Rather than offering only general opinions of their overall experience, this type of language processing can break your product or service into discrete parts and identify which ones are included in a particular opinion found in a text. For example, assume that many of your customers are dissatisfied with your SaaS specifically because of an issue with an API. Aspect-based sentiment analysis will let you know immediately that the problem is specifically with the API, instead of telling you that customers are unhappy with your product in general but leaving you in the dark on the reason why.
Multilingual sentiment analysis
Finally, multilingual sentiment analysis can provide information on opinions from texts written in different languages. This can often be somewhat difficult since multilingual analysis usually requires not only literal translations of the text but an AI-based recognition of idioms and nuances of the language in question. However, recent developments in language processing technology now allow you to get more coherent and accurate data on opinions expressed in languages other than English. This is especially useful if you, for example, market your services to other countries or have a large Spanish-speaking contingency in your customer base.
Opinion Mining Techniques
As with any form of natural language processing, opinion mining features several different techniques that help the process better understand textual opinions and avoid language pitfalls. One of the biggest issues with natural language processing is non-literal text. When expressing opinions, especially online, few people are strictly literal, instead employing things like sarcasm, metaphor, slang, and idiomatic expressions. Contemporary opinion mining processes have developed a few key techniques that help integrate natural language processing technology with natural human speech and writing.
- Manual coding: Here, you would look at each data point from a text individually to identify different sentiments within the overall language processing system. Manual coding can be effective but is also quite time-consuming.
- External crowd platforming: You outsource your language processing to an external platform for human subjects to identify and label opinion expressions within the framework of the natural language processing system.
- Integrated approach techniques: In this process, one natural language processing platform includes data collection and opinion mining.
Different machine learning processes that mine opinions also require different techniques, ranging from supervised machine learning, semi-supervised and unsupervised machine learning, and even deep learning. Which technique works best for you depends on your business, what data you are looking for, and your available resources.
Overall, opinion mining provides an excellent means of understanding and assessing the broad opinions of your customer base in real-time. However, given the complex machine learning capabilities needed for opinion mining to work and given that accurate results require extensive web-scraping, effective opinion mining may be beyond the resources that you currently have at your disposal.
Therefore, when considering opinion mining for your business, it’s a good idea to partner with a top service that comes with specific expertise in opinion mining and web scraping. Scraping Robot, for example, provides exceptional web scraping service packages that afford you and your company the best opinion mining options without high overhead costs. No more worrying about blocks, captchas, proxy management, or browser scaling! If you run a business and are looking to utilize opinion mining to improve your services, take a look at Scraping Robot’s numerous, convenient web scraping options for the best plan for you!
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