Sentiment analysis or opinion mining is one of the trending concepts associated with NLP. The ability of this application to identify the emotional tone behind a body of text has gained tremendous popularity among organizations seeking to determine and categorize their customer opinions about a product, service, or feature. Today businesses are driven by huge volumes of customer data from multiple sources. Harnessing the power of sentiment analysis to evaluate customer insights is helping businesses to pre-determine the success of the application.
How? We know the success of any product completely depends on the likelihood of a customer. If a customer likes a product, the business will experience growth, and if not the business needs to make necessary strategies to scale its growth. However, leveraging sentiment analysis to perform the pre-market research and analyzing customer insights can eliminate the time associated with market trials and the cost associated with product development.
What is Sentiment Analysis (opinion mining)?
Sentiment analysis is a field of study that uses natural language processing, text analytics, computational linguistics, and biometrics to systematically identify, study, and analyze the affective states and subjectivity associated with information. It classifies the outcome of a given study as either positive, negative, or neutral to define the objectivity behind a piece of information after analysis. In addition to identifying the sentiment, opinion mining can also extract the polarity (the amount of positivity or negativity) associated with the text. And as a bonus, sentiment analysis can also apply to understand the objectivity of varying content scopes such as document, paragraph, sentence, and sub-sentence level.
Using sentiment analysis organizations can process and gather information from both unstructured and semi-structured text sources. The three most unified ways to leverage sentiment analysis are rule-based, automatic application, or hybrid application. The rule-based system application is bound to perform sentiment analysis based on predefined or lexicon-based rules, while automatic systems learn from data with machine learning techniques at the core. A hybrid approach follows a mix of both techniques to achieve the highest possible level of accuracy.
Types of sentiment analysis
- Fine-grained sentiment analysis: Analyzing the sentences by parts, a sentence is broken into phrases or clauses, and each part is analyzed in a connection with others. This analysis provides a precise level of polarity between very positive to very negative. One can identify who, what, and where a person is talking about a product, service, or idea.
- Emotion detection sentiment analysis: Emotion analysis aims to identify and recognize types of emotions through the expressions of text, such as happiness, anger, disgust, fear, sadness, and surprise.
- Aspect-based analysis: ABSA is a type of text analysis that categorizes opinions by aspect and identifies the sentiment related to a particular text. For instance, fragrance and price are two attributes related to a product scented candles, where the price is negative and the fragrance is positive. The goal of sentiment analysis here is to identify the two aspects fragrance and price – and their related sentiment (fragrance: positive, price: negative) For example: “This candle holds mild fragrance at this price point”, notice that in the same text, we have different sentiments, where aspect-based analysis aims to provide a more granular and detailed level of information regarding the sentiment.
- Intent analysis: This approach is a step ahead of sentiment analysis. While the latter is a common text classification tool that analyses an incoming message and tells whether the underlying sentiment is positive, negative, or neutral. Intent sentiment analysis informs you all about the user's intention behind the message and necessary actions to be considered in advance.
The most popular way to apply sentiment analysis
Sentiment analysis tools can be used by organizations for a variety of purposes, and the applications associated with leveraging customer insights are numerous. Below we have mentioned the most common ways one can use sentiment analysis to empower business objectives by harnessing customer sentiments.
Social media monitoring
Irrespective of the industry you work for – retail, finance, tech, health, or government – you probably receive a lot of feedback on social media, digital platforms, and online forums. And, you’re looking at hours, maybe even days, to process all that data manually to optimize your business approach. However Social media posts often present some of the most truthful points of view about products, services, and businesses because users offer their opinions unsolicited. Sentiment analysis can analyze individual emotions and overall public sentiment on every social platform for better management and optimization.
Customer support and feedback
Customer support and feedback assistance management present many challenges due to the long waiting number of requests, varied topics, and diverse branches within an organization – not to mention the urgency of any given request. Sentiment analysis with natural language understanding (NLU) reads regular human language for understanding the meaning, emotion, expression, and more, to analyze customer requests, just as a person would. You can automatically process customer support tickets, online chats, phone calls, and emails via sentiment analysis, which might also indicate urgency and route to the appropriate team for effective communication with the customer and improve the satisfaction index.
Brand monitoring and reputation management are considered one of the most popular applications of sentiment analysis. With Sentiment analysis tools, you will be notified about negative brand mentions on distinct social forums, online chat groups, blogs, press, etc., in real-time. Not only that, you can keep track of your brand’s image and reputation over time or at any given moment, so you can monitor your progress and scale growth opportunities. An organization can successfully transform this data into usable information and statistics for effective business decisions and outcomes.
Discover how a product or service can be perceived by your target audience. Which elements of your product or service need to be improved to gain market dominance? Sentiment analysis provides better results with utmost accuracy and it’s not subjective. By harnessing the power of sentiment analysis an organization can search for keywords of a particular product feature (interface, UX, functionality) and train sentiment analysis models to find only the information needed to optimize the expected results.
Market Competitive analysis
Another prominent application of sentiment analysis is defining the strengths and weaknesses of your product or service through market research and competitor analysis. Through analyzing the customer data, find out who’s trending among your competitors and how you can improve your marketing efforts to turn negatives into positive outcomes.
Voice of employee
Sentiment analysis software allows you to analyze your employee's opinions subjectively, with no human interference. By evaluating the employee surveys or analyzing the reviews on various job portals, emails, and employee connectivity platforms you can discover the most common concerns to keep your employees happy, engaging, and productive.
In this article, we have provided a quick guide to sentiment analysis and how you can trade your customer insights to develop go-to-market strategies for increased sales of a product, service, or idea. With limitless opportunities to leverage sentiment analysis and data analysis tools, businesses can perform tasks that were previously unthinkable. To know more about sentiment analysis applications and how you can scale this technology for your business, we are ready to help. For a free consultation contact us.