Sentiment Analysis - Mining For Opinions
Sentiment analysis is a process to analyze content to detect its sentiment, whether it is positive, negative, or neutral. This is why it is also known as opinion mining. Sentiment analysis is mostly used for text content to understand customer/user sentiment about targeted brands and products. It helps businesses better understand their customers’ needs and modify their products and services according to the feedback.
Types of Sentiment Analysis
Sentiment analysis primarily deals with the polarity of the targeted text i.e. if it's positive, negative, or neutral, but it can also be used to go deeper into understanding the text and detect feelings, emotions, and intentions within the text.
Sentimental analysis is a multi-faceted technique and depending on what type of data you need about customer sentiment, the type of sentiment analysis changes. Given below are some of the more common techniques of sentiment analysis:
Graded Sentiment Analysis
If the focus of your business is on polarity detection then your sentiment analysis should have expanded polarity categories. So, in addition to the positive, negative and neutral polarity you should now have extended categories of very positive & very negative.
This is known as graded sentiment analysis.
Emotion detection
Emotion detection is a sentiment analysis technique that can be employed to detect a wide array of emotions in text. Emotion detection techniques either include the use of lexicons or complex machine learning. In this context, lexicons represent a list of words and the emotions they represent.
The superior way to approach emotion detection is by using machine learning instead of relying on the use of lexicons. The issue with lexicons is that some words are used to describe different emotions, which can cause a lack of clarity in the results. For example, the word kill can represent negative emotions (your services are killing me right now!) or it can represent positive emotions (your services are killing it right now!)
Aspect-based Sentiment Analysis
When you want to know the specific aspect of your services or products that people are expressing their sentiments about, you can use aspect-based analysis for this task.
Let us explain with an example. Consider this review of a product:
“ The camera quality is really good, but the build-quality is lacking”
From this review, an aspect-based sentiment analysis will be able to deduce that the customer is positive about camera quality but expresses negative emotions about its build-quality.
Multilingual sentiment analysis
Performing sentiment analysis in more than one language is known as multilingual sentiment analysis and it is one of the harder techniques as it involves a higher degree of processing and requires more resources.
How Sentiment Analysis Works
Sentimental analysis is made possible using NLP (natural language processing) and machine learning algorithms.
Emotional tones behind online interactions are determined automatically using sentiment analysis and there are different algorithms in sentiment analysis for different scenarios in data mining. Generally speaking, there are three main categories of these algorithms:
Automatic: These sentiment analysis systems use advanced machine learning to analyze data.
Rule-based: These sentiment analysis systems use an automatic process bound by a set of manually specified rules to deduce results from data.
Hybrid: These sentiment analysis systems are an amalgamation of both the automatic and the rule-based analysis systems.
The Importance of Sentiment Analysis For Businesses
With the advent of information technology and the age of social media, we are seeing an unprecedented level of engagement between humans from all over the world. It is only natural that in such a world the importance of sentiment analysis is increasing rapidly as a tool for monitoring and deducing results from this data.
You can probably say that no one can understand the importance of sentiment analysis better than businesses. Analyzing customer feedback, opinions and suggestions automatically and deducing their emotional response from this data can help businesses improve their products and services.
Furthermore, an important utility of sentiment analysis for businesses is getting market insights.
Getting Market Insights Through Sentiment Analysis
Sentiment analysis is an immensely powerful tool for getting marketing insights and making competitive analyses. You can use sentimental analysis to:
- Analyze reviews of your products and services and make comparisons with the reviews of your competitors’ product reviews to find shortcomings and improve your products.
- Monitor your business and your competitors simultaneously in real time. This can allow you to recognize new opportunities as new markets that can be potentially profitable.
- Keep up with new trends and align your business to established biases.
Real-World Applications of Sentiment Analysis
In today’s world of 0s and 1s, the applications of sentiment analysis of data are innumerable. Below are some practical examples of sentiment analysis techniques used in different fields:
Crime prevention
In our world today, human lives are documented as they have never before in the history of mankind. Every aspect of our lives is documented through social media and internet interaction, this includes malicious intent and activity. Law enforcement agencies can use sentiment analysis techniques to identify potential crimes and threatening behavior online and act on this data to prevent any tragedies from happening.
Investment intelligence
The world of trading and investment banking needs its eyes and ears on the daily happenings around the world. As the ever-shifting geo-political situation directly affects stock markets around the world, it is important that trade agencies are always up-to-date on these events.
Sentiment analysis can help investors analyze the text from news articles, blogs, and social media posts about certain products and deduce the emotional sentiment of the public about their target, which then enables them to make informed investment decisions.
Customer Service
In a market that is extremely competitive when it comes to product variety, design and quality, the only way to gain an edge is customer service. Your customers expect a pleasant, intuitive, personal and hassle-free experience and if they can’t have that then they’ll just do their business elsewhere. So, how does sentiment analysis help you in improving your customer service?
Why Should Businesses Prioritize Sentiment Analysis
We are living in the 21st Century a.k.a the age of information technology. In our world today there is more knowledge accessible to every single human being than it was ever before in our history.
With the right tools to harness the power of this knowledge, we can achieve what seems impossible to some. Sentiment analysis is one such tool to fully realize the potential of the available data, and any business that prioritizes it can benefit immensely!
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