5 Key Business Applications of Sentiment Analysis

In order to gauge whether your customers are happy with what you’re doing, customer satisfaction is crucial. It is proven that a high degree of satisfaction increases the lifetime value of a customer, more customer retention, and a stronger reputation of the brand. Alternatively referred to as “opinion mining,” sentiment analysis can help product managers understand the customer satisfaction level.

Analyzing customer sentiments to predict accurate sentiments has been challenging and time-consuming due to the high volumes of collected data from various sources. Having an NLP and AI expert on hand can help you get started with sentiment analysis if you want to understand your customers in a dynamic market.

What Goes into Procuring Sentiment Analysis

Artificial Intelligence algorithms, Natural Language Processing (NLP) methods, and Machine Learning (ML) models contribute to sentiment analysis. This includes neural networks, unigram algorithms, skip-gram algorithms, and bag-of-word regression models using machine learning and deep learning (DL). The use of natural language processing techniques in machine learning software allows unstructured texts to be sorted by emotion and opinion.

As an example, let’s look at this. Imagine that you are responsible for understanding the general sentiment in the support conversations at your company. Get a sense of your customers’ ongoing mood and tone. You would need an emotion classifier in such a situation to produce predictions regarding relevant conversations. Dashboards that are often designed to measure customer satisfaction can be populated with sentiment data you’ve collected from your customers.

Why is Sentiment Analysis Important in Business?

At first glance, it may seem like a fancy class project, but sentiment analysis has many practical applications in the business world. Using sentiment analysis to solve business problems is an excellent starting point.

  1. You can aggregate customer sentiment from free-form feedback data and identify the primary promoters or detractors of your product or service. Once the detractors have been converted to promoters, you can gradually implement corrective measures to rebuild trust.
  2. It is possible to detect hateful and inappropriate comments on your online platform, which will help you keep it free of bullies.
  3. The feedback you receive from recent employees, peer reviews, and manager feedback can help you identify demotivated or about-to-quit-employees and provide them with constructive ways to succeed.

Based on these sentiment analysis examples, you can see that sentiment analysis is an excellent tool for gaining a better understanding of employees and customers, keeping platforms safe, improving customer shopping, and learning from competitors. Sentiment analysis can be combined with other artificial intelligence technologies like text summarization to provide deeper, more powerful insights.

Industry Applications of Sentiment Analysis

In sentiment analysis, the text is analyzed to identify and extract subjective information, which businesses can use to measure how customers feel about their service offerings. Using AI-driven technology, sentiment analysis traces the undertone of text by channeling extensive digital information.

The use of sentiment analysis is widespread across various industries. Several popular sentiment analysis applications exist, such as monitoring social media, managing customer support, and reviewing customer feedback. Here are a few more to get you going with the idea of integrating sentiment analysis into the marketing & customer analysis symmetry of your business:

  1. Social Media Monitoring

Social media posts often contain some of the most honest opinions about your products, services, and businesses because they’re unsolicited. By incorporating NLP experts on board, you can analyze individual emotions and overall public sentiment across all social platforms based on their comments, feedback, and reviews.

In chat, sentiment analysis can detect sarcasm, understand acronyms (OMG, LOL, ROFL, etc.), correct misspellings and misuses, and detect sarcasm.

  1. Managing & Monitoring Business Reputation

Sentiment analysis is widely used in business for brand monitoring. A business can leave negative reviews online for a long time, and the longer it does so, the worse the situation will become. Negative brand mentions can be detected as soon as they occur with sentiment analysis tools.

Furthermore, you can monitor your progress over time by keeping track of your brand’s image and reputation. You can use this information to develop usable statistics and information about your brand, whether you are monitoring news stories, blogs, forums, or social media for information about your brand.

In addition to following trends and anticipating outcomes, machine learning can be relied upon to stay one step ahead of the competition and become proactive rather than reactive.

  1. Analyzing the Market Response to a Product

Learn what users are saying about your new product soon after launch, or examine feedback you may have yet to see. Aspect-based sentiment analysis allows you to locate only the information you need based on your search terms (interface, UX, functionality). Identify what makes your most valuable customers happy, learn how your product is perceived by your target audience, and improve your product. The whole thing is based on sentiment analysis.

  1. Get to Know the Voice of Your Customers (VoCs)

Creating a personalized customer experience requires communication and listening to the voice of your customers. As a result, businesses have access to a large number of conversation sentiment analysis datasets that can be integrated into the development of ML models in order to automate the sentiment analysis process. Taking all feedback, you receive from customers, such as web feedback, surveys, chats, and customer service calls, and evaluating it. This data can be categorized and structured with the help of sentiment analysis so that you can discover recurring topics and concerns.

  1. Analyzing Market & Competitors

In order to research the market and your competitors, you can use sentiment analysis. Get a sense of who your competitors are mentioning positively and how your marketing efforts compare. Look at how your competitors speak positively to their customers, and take some of those phrases and use them in your own brand message and tone of voice guide.

Advantage of Manual Sentiment Analysis Over AI-Integrated Machines & Apps

In a world of billions of people, each communicates uniquely, e.g., adding tiny variations to the language and attaching sentiments to it, which are easy for us to understand but difficult for machines to understand. Sarcasm, for instance, is the opposite of what is true when we use it in our sentences. Sentiment analysis through machine learning models is difficult to predict customer behavior, as a matter of fact. Therefore, manual sentiment analysis has an advantage over automated sentiment analysis performed through AI-integrated applications and machines.

Final Words

For accurate sentiment analysis results, a blended approach to sentiment analysis is the most effective way for businesses to utilize it to understand customer behavior. Consequently, Cogito, an NLP expert, combines manual and automated approaches for accurate sentiment analysis.

In addition to manually conducting sentiment analysis of your social media channels and customer feedback on other platforms, the experts here can also assist you in getting AI training data in order to build ML models for automated sentiment analysis based on real data rather than assumptions made based on a small sample.

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Sentiment Analysis: Types, How it Works & Why it is Difficult?

Understanding the sentiments of the people is not easy unless they express their feelings, opinions and perspective anything. But if you have such platforms where people are freely speaking up about their thoughts and concerns, you can easily find out their sentiments. Here where Cogito comes in the facility of sentiment analysis.

Sentiment Analysis is the process of determining the conceptions, judgments, feelings, opinions, viewpoints, conclusions, and other notions towards anything. It is a technique to analyze texts, images, emojis and various other actions to know what other people think about a product, service, company, brand name, or a reaction to a specific event, social movement, etc.

The Usefulness of Sentiment Analysis

Sentiment analysis is playing an enormous role in understanding people belonging to different groups and their sentiments. On political grounds, it helps to know how much of the majority is in favor of the Govt. or how many stands opposing to their services and measures undertaken.

While on the other hand, in the business world, it is helping companies to know their customers in a better way. Such a resource becomes useful for the business enterprises to offer products and services as per the expectations of their potential customers and get appropriate results.

Social Media is one the best and biggest platforms where the theory of sentiment analysis is and must be applied, to interpret the feelings of various people. Hence, we need to understand it as a process, how it works, its applications, and why it is important for business organizations and other aspects.

What is Sentiment Analysis in NLP?

Natural Language Processing (NLP) is a way to understand the actual meanings of the text words, sentences, or entire written documents. NLP is used to train the machines thereby, helping them understand the language and communication process among humans while discussing a topic.

The main motive of sentiment analysis is to find out expressions of people that are eventually classified as positive, negative, or neutral. It can be used in diverse areas such as company products, market research, marketing analysis, customer targeting, product reviews, customer feedback, reputation management, etc.

How Does Sentiment Analysis Work?

As mentioned above, sentiment analysis is used in NLP-based Machine Learning algorithms to develop such AI applications that can understand the ways of linguistic context showing the sentiments of different people.

But the question here is, how does sentiment analysis work? The developers begin by creating a text Machine Learning-based algorithm that can detect the contents showing any specific sentiment indicator.

Afterward, they train the ML classifier by feeding it a huge quantity of training datasets containing reactions based on positive, negative, and neutral sentiments. Every piece of content is scattered and divided into basic components such as text words, phrases, sentences, and other entities.

Once this process is completed, the relationship between the topics and the identification process commences. Then, AI model assigns a sentiment score to that particular post. The post can range from 1 representing negative and +4 representing 4 positive comments. If sentiment is neutral, the score is usually given 0.

As we already know, understanding the different human languages is a very complex task due to their cultural and social diversity. Hence, it is important to train the programs in such a way that they are able to detect and analyze grammatical nuances.

TYPES OF SENTIMENT ANALYSIS

To understand the sentiments of people, there are different types of sentiment analysis used in the market. Apart from normal opinions – positive, negative, or neutral, other types of sentiment analysis help in understanding people’s inner feelings, their actual intentions, and emotions.

Fine-grained Sentiment

This is one of the most simple and common ways of understanding your customers’ sentiments. Yes, fine-grained sentiment analysis helps in studying the ratings and reviews given by the customers.

While analyzing the sentiments, you can use the readily available categories like very positive, positive, neutral, negative, or very negative. Providing a rating option from 1 to 5 is yet another way to scale the feedback given by your customers. Most e-commerce sites use this technique to know the sentiments of their customers.

Aspect-based Analysis

This type of sentiment analysis is more focused on the aspects of a particular product or service. To make it easier to understand, let’s take an example – if you are talking about a soundbar or a wireless speaker system.

Here you can analyze your customer’s sentiments by asking them for feedback about the sound quality, connectivity, and other features, making such devices more useful and productive for the users. It helps in determining specific attributes of the product.

Intent-based Sentiment Analysis

To know the intent of the customers; whether they are looking to buy the product or just browsing around, is achievable through intent analysis. It not only helps to identify the intent of the customers but also to track and target them through advertisements or other ways of online promotions.

With intent analysis, companies can save their time, efforts, and cost while targeting the potential customers as per their intentions. This helps in getting a more vivid understanding of the intentions of the customers.

Emotion Detection Sentiment Analysis

As the name symbolizes, this type of sentiment analysis helps to detect and understand the emotions of the people. Emotions like anger, sadness, happiness, frustration, fear, panic, worry, or anxiety, may all be included.

Understanding the sentiments of people using emotion detection is even more difficult as people use a collection of words having a different senses of meanings. Hence, inaccurate emotion detection can lead to an inaccurate decision while analyzing the true sentiments of the people.

How Can Sentiment Analysis be Used?

The main motive of using sentiment analysis is to find out the true feelings of the varied people living in our society. It can be used for analyzing the customer’s feedback of a particular company, normal users on social media towards a product, service, social issues, or political agendas.

Companies also use it for brand analysis, reputation crises, campaigns performances, competitor analysis, and improve the service offered to the customers. Analyzing the sentiments of the customers helps the customer support team to prioritize their work for offering better service to end-users.

Why Sentiment Analysis is Difficult?

Sentiment analysis is a very difficult task due to sarcasm. The words or text data implied in a sarcastic sentence come with a different sense of meaning depending on the sender or situation.

Sarcasm is remarking someone opposite of what you want to say. It is expressed to hurt someone’s feelings or humorously criticize something. On social media, sarcasm is one of the most common behavior you can see nowadays interfering with the results.

Sarcastic words or texts show the unique behavior of people. It is usually used to convey a negative sentiment using the positive intention of words. This kind of caustic remark can easily mislead the sentiment analysis decisions.

The presence of sarcastic words makes it difficult for sentiment analysis processing in turn making it difficult to develop NLP-based AI models. Hence, a deeper analysis of such words is required to understand the true sentiments of people with accuracy.

In such a case, we can use the psychographic-based analysis to understand such people and their exact intention of what they are trying to say. Using psychographic segmentation in sentiment analysis can give a more comprehensive perception of different kinds of people interacting with each other.

Sentiment Analysis using Psychographic Segmentation

Sentiment analysis can be a big game changer in forming a more focused marketing strategy for the companies. But these establishments need to understand their customers by segmenting them into their characteristics like their cultural values, beliefs, desires, goals, interests, and lifestyle choices.

Psychographic Segmentation becomes helpful here in analyzing the customers’ sentiments by segmenting them based on their activities, lifestyle, and interests. It is a more qualitative approach to studying consumers according to their psychological characteristics.

Psychographic segmentation not only helps in improving the customer experience but also authorizes companies to offer more tailored products or services to the right customers at the right time improving the return on investment.

Psychographics allows you to learn about the deeper motivations and emotions that influence potential customers. Hence, in the next article, we will learn more about psychographic segmentation, and how it is helpful in sentiment analysis.

Cogito offers sentiment analysis services for wide-ranging industries using the insights of our skilled workforce making every interpretation meaningful. From social media to other useful online platforms, we are experts in analyzing the sentiments of different groups of people towards different entities.

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Social Media Sentiment Analysis Using Twitter Datasets

Several hundreds of thousands of raw data files are uploaded by users every day to social media sites. Online user data provides access to an enormous amount of information regarding products, services, places, and events, which makes it suitable for sentiment analysis. Valuable information can be extracted by analyzing the sentiment of the data.

It is a method for interpreting opinions within a text that uses Natural Language Processing (NLP) to extract positive, negative, and natural meanings from user-generated content shared on social media platforms. Sentiment analysis has been previously applied to products or movie reviews to understand customers’ interests better and, thus, improve outcomes and service offerings. Sentiment Analysis Types/Levels & Methods

Experts define sentiment analysis with three categories/levels, i.e., Sentence level, Document level, and Feature level. This exercise classifies positive and negative sentiments from a sentence, document, or feature.

It has been determined that two main sentiment analysis methods exist: machine-learning techniques and lexicon-based techniques. The machine learning approach focuses on extracting and detecting sentiments from data, while the lexicon-based approach counts positive and negative words that appear in the data.

Social Media/Networking Sites and Types based on Content Sharing

There are many social networking sites for users to share their minds, life stories, narratives, and opinions online. There are four different types of social media sites, based on what types of content users are allowed to share online:

  • Content communities (Youtube, Instagram);
  • Social networking (Facebook, LinkedIn),
  • Blogs (Reddit, Quora);
  • Micro-blogging (Twitter, Tumblr).

Microblogging sites are the most common social media sites to extract information for sentiment analysis. These micro-blogging social sites, particularly the likes of Twitter, are found to be the best for gathering insights from user-generated posts and opinions shared online.

The online ecosystem of social media platforms is getting more mature as time goes by. Social media platforms are turning out to become an ocean of valuable information. Each social media content might be different, and it is worth exploring the right social media platform to source or extract users’ information. However, bloggers, WordPress, YouTube, and other social media sources are given much less attention when extracting insights from users’ opinions shared online.

The most popular social media site to extract information is Twitter. Most of the reviewed papers use Twitter as their social media context. This is due to the availability, accessibility, and richness of Twitter content. Twitter enables users to post and interact with short messages allowing them to express opinions. There are millions of tweets every day on almost any topic. The available content or data for public use makes Twitter popular and the best social network for sentiment analysis.

Twitter — Why It’s Best for Sentiment Analysis

The user-generated content on Twitter can provide valuable information to scholars, business organizations, and the government. Twitter conducts real-time analysis and closely public sentiment as Twitter has about 500 million tweets per day, and it allows public access to its data through API. Twitter is used to search and collect tweets from 8 different countries from western and eastern countries. There is Twitter user all around the world, thus making it rich with opinion and views by people from other countries, languages, and perceptions.

Application of Sentiment Analysis

Application context of sentiment analysis The application of sentiment analysis ranges from business and marketing, politic, and health to public action. Sentiment analysis is not limited to one application but provides a vast application in different areas to assist decision-making. Sentiment analysis can be applied to events, activities, sports, or worldwide disasters.

Here are some significant applications of sentiment analysis to look into. Sentiment analysis can:

  1. raise awareness of data security and the danger of security breaches;
  2. acts as a guideline for companies to respond to security breaches in shaping public perception;
  3. Identify people’s sentiment needs during a disaster and prepare an appropriate rescue & response plan
  4. Help find the level of depression of a person by overserving and analyzing emotions from the text.

Furthermore, sentiment analysis was conducted on social media’s unemployment rate and employment sentiment score. We can see the application of sentiment analysis in healthcare and where the study uses Sentiment analysis as a service framework proposed and utilize Spatio-temporal properties to identify locations of disease outbreaks. Political elections can be predicted with sentiment analysis based on Twitter data. There is a 94% correlation between Twitter data and polling data, which indicates that users’ opinions on the social sharing platform are more reliable than polling data. Twitter may therefore be an even more reliable polling method than sophisticated methods.

Choosing the Right Social Media for Sentiment Analysis

Facebook boasts of being the largest social sharing platform by user numbers. But sentiment analysis through Facebook is not feasible as the data here is too messy to extract. The user-generated content on the platform needs to be structured better.

Users often commit spelling blunders and errors in sentence framing while posting content on Facebook, making the data hard to extract any context. The case is the same with other significant content and social sharing platforms. This puts microblogging/micro-sharing sites in a better place as the best choice for sentiment analysis.

Studies show that 88% of the data comes from Twitter. The other source of social media is not preferable because the number of data or opinions that can be extracted is limited, such as in Blogspot, YouTube, and WordPress.

How Social Media Sentiment Analysis Makes a Business Impact

Businesses gain significant advantages from sentiment analysis by identifying how customers perceive their products or service—assessing the effectiveness and capability of social media and business brand communication and analyzing the flow of businesses and stock prices.

It is displayed by a study comparing sentiment data on Big Six consumer tweets (Britain’s largest and oldest gas and electricity supplier and new entrant energy consumer). The result indicates that the sentiment from the Big six is more negative than a new entrant energy consumer. In addition, sentiment analysis on social media allows the organization to evaluate the success level of a program, as shown in a study where a high positive sentiment is obtained from a tweet on community development program activity. The result can help improve the community’s overall living standard.

conclusion

There are many applications for sentiment analysis, including improving quality and strategy within a business, forecasting elections, monitoring disease outbreaks, increasing awareness of the importance of data security, improving perception towards a particular sport, and locating and responding to disasters more effectively.

Now that sentiment analysis helps us understand people’s perceptions and make decisions, we have realized how important it is. However, developing a universal sentiment analysis model can be applied to a wide range of data types. Exploring other social networking sites to obtain user opinions and expanding the context of sentiment analysis applications are essential for future recommendations.

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How to Data Sets Annotated for Sentiment Analysis in the News Headlines?

To make understandable the sentiments of the people by the AI model, a huge amount of training data sets required for machine learning. And language annotation is the right process to annotate the key texts in a document and make it comprehensible to machines.

Continue reading “How to Data Sets Annotated for Sentiment Analysis in the News Headlines?”

What Are the Different Types of Sentiment Analysis?

To understand the mindset of various people through online sources, sentiment analysis is one of the best option you can use. And social media content moderation is the right online platform where sentiment analysis process can be used to analyze the sentiments of the people and know their feelings and opinions.

Continue reading “What Are the Different Types of Sentiment Analysis?”

How Can Sentiment Analysis be used for Brand Management?

Sentiments of the people decide what they are thinking or what is running into their mind towards a particular company, its product or services or a particular brand. And sentiment analysis helps analyze the opinions and feelings of the different people that can be later used for brand recognition and management for better competitive position in the market.

Continue reading “How Can Sentiment Analysis be used for Brand Management?”