Machine learning, natural language processing (NLP) and other data analysis techniques are used in sentiment analysis in order to assess and obtain objective quantitative findings from raw text. Discover how sentiment analysis works, the various types, and how you can use it to improve operations and decision-making, as well as analyze customer satisfaction.
What is sentiment analysis?
Sentiment analysis is the method of recognizing positive or negative sentiment in text. Businesses use it to detect sentiment in social data, reputation management, and learn more about their customers.
As clients communicate their ideas and sentiments more openly than ever before, sentiment analysis is becoming a critical tool for monitoring and understanding client sentiment. By automatically analyzing consumer feedback, such as comments in survey responses and social media dialogues, brands can understand what makes customers pleased or upset. This enables them to tailor products and services to meet their needs.
Using sentiment analysis to automatically scan thousands of reviews about your product, for example, can help you figure out if your customers are satisfied with your pricing and customer service. Perhaps you’d like to track brand sentiment on social media in real time and over time so you can see angry customers right away and respond quickly. Sentiment analysis has a plethora of uses.
Types of sentiment analysis
Models of sentiment analysis concentrate on polarity (positive, negative, and neutral), urgency (urgent, not urgent), feelings and emotions (angry, sad, etc.), and intents (purchase, recommend, not purchase, etc). You can define and customize categories to match your sentiment analysis needs, depending on how you wish to interpret client feedback and inquiries.
Here are the most common sentiment analysis types:
Multilingual sentiment analysis
Sentiment analysis in multiple languages can be challenging. It necessitates a significant amount of pre-processing and resources. Many resources are available online (for example, sentiment lexicons), while others must be generated (translated corpora, etc.), but you need to know coding to be able to use them.
Alternatively, you might use a language classifier to detect language in texts automatically, then train a custom sentiment analysis model to classify texts in the language of your choice.
Sentiment analysis of this type seeks to detect emotions such as frustration, happiness, sadness, and so on. Many emotion recognition systems rely on lexicons (lists of words and the feelings they evoke) or sophisticated machine learning techniques.
People communicate emotions in a variety of ways, which is one of the drawbacks of employing lexicons. Some words that are commonly used to communicate anger, such as “bad” or “kill” (for example, “your product is so horrible” or “your customer service is killing me”), can also be used to express satisfaction (e.g. “this is bad ass” or “I hate how good this is”).
Fine-grained Sentiment Analysis
If polarity precision is crucial to your business, you should consider adding the following polarity categories:
- Mostly Positive
- Mostly Negative
Fine-grained sentiment analysis is what this is known as, and it can be used to analyze 5-star ratings in a review, for example:
- 5 stars for very positive
- 1 star for very negative
Aspect-based Sentiment Analysis
When evaluating text sentiments, such as product evaluations, you typically want to discover which specific parts or features people are citing in a good, neutral, or negative light. An aspect-based classifier would be able to determine that the sentence indicates a negative judgment about the feature “battery life” in this text: “The battery life of this camera is too short.”
Who uses sentiment analysis?
Sentiment analysis is used in a wide range of applications and for a variety of reasons. On Twitter, for example, sentiment analysis can be used to estimate overall opinion on a popular issue. Sentiment analysis is frequently used by businesses and brands to monitor brand reputation across social media platforms and the Internet as a whole.
Monitoring call center and omnichannel customer service performance is one of the most extensively used applications for sentiment analysis. Sentiment analysis is increasingly being used for general brand monitoring as organizations want to keep a finger on the pulse of their audiences.
Political candidates and administrations have leveraged sentiment analysis to track public opinion on policy changes and campaign announcements, allowing them to fine-tune their strategy to better connect with voters and constituents.
Why is sentiment analysis important?
Sentiment analysis is important because it allows businesses to understand how their customers feel about their products or services. Businesses can make better and more informed decisions by automatically classifying the sentiment behind social media conversations, reviews, and more.
90 percent of the world’s data is unstructured (MIT Management Sloan School, 2021), or unorganized, according to estimates. Every day, massive amounts of unstructured business data are generated (emails, support tickets, chats, social media interactions, surveys, publications, documents, and so on). However, analyzing sentiment in a timely and efficient manner is difficult.
How does sentiment analysis work?
Sentiment analysis, also known as opinion mining, uses natural language processing (NLP) and machine learning techniques to assess the emotional tone of online conversations automatically.
Depending on how much data you need to analyze and how accurate you need your model to be, you can use a variety of algorithms in sentiment analysis models. Below, we go over some of these in greater depth.
There are three types of sentiment analysis algorithms:
- Rule-based: these systems use a set of manually defined rules to perform sentiment analysis automatically.
- Automatic: To learn from data, systems rely on machine learning algorithms.
- Hybrid systems are sentiment analysis systems that mix rule-based and autonomous approaches.
1. Rule-based Approaches
A rule-based system typically relies on a set of rules to aid in the identification of subjectivity, polarity, or the subject of an opinion. Various NLP techniques established in computational linguistics may be included in these rules, such as:
- Stemming, tokenization, part-of-speech tagging, and parsing are some of the techniques used.
- Lexicography (i.e. lists of words and expressions).
Here’s how a rule-based system works in practice:
- Two lists of polarized words are defined (e.g. negative words such as bad, worst, ugly, etc. and positive words such as good, best, beautiful, etc.). The number of positive and negative terms in a given text is counted.
- The system returns a positive sentiment if the number of positive word appearances is more than the number of negative word appearances, and vice versa. The system will return a neutral emotion if the numbers are equal.
Rule-based systems are extremely unsophisticated since they ignore how words are combined in a sequence. Of course, more advanced processing techniques and new rules to support new expressions and language can be implemented.
Adding new rules, on the other hand, may have an impact on prior results, and the system as a whole might become highly complex. Because rule-based systems frequently require fine-tuning and maintenance, they need to be reinvested in on a regular basis.
Unlike rule-based systems, automatic methods rely on machine learning techniques rather than manually constructed rules. A sentiment analysis problem is typically described as a classification problem, in which a classifier is fed a text and outputs a category, such as positive, negative, or neutral.
In a machine learning text classifier, the first step is to modify the text extraction or text vectorization, and the traditional method has been to use a bag-of-words or bag-of-n-grams with their frequency.
Recently, novel feature extraction algorithms based on word embeddings have been used (also known as word vectors). This type of representation allows words with similar meanings to have comparable representations, which can help classifiers perform better.
A statistical model such as Nave Bayes, Logistic Regression, Support Vector Machines, or Neural Networks is commonly used in the classification step:
- With Nave Bayes, Bayes’ Theorem is used to predict the category of a text in a family of probabilistic algorithms.
- Linear Regression is a well-known statistical approach for predicting a value (Y) given a set of features (X).
- Support Vector Machines (SVMs) are a non-probabilistic model in which text instances are represented as points in a multidimensional space. Different categories (sentiments) are assigned to different areas within that space. Then, based on similarities with existing texts and the regions they’re linked to, new texts are allocated a category.
- Deep Learning is a collection of techniques that use artificial neural networks to process data science in an attempt to replicate the human brain.
Hybrid systems integrate the benefits of both rule-based and automatic procedures into a single system. One of the major advantages of these systems is that the results are frequently more accurate.
Sentiment analysis applications
Sentiment analysis has numerous applications in a variety of industries, ranging from finance and retail to hospitality and technology. We’ve compiled a list of some of the most common ways sentiment analysis is leveraged in business:
Brand monitoring provides a plethora of information from conversations about your brand that are taking place all over the internet. Analyze news stories, blogs, forums, and other sources to gauge brand sentiment and, if appropriate, target certain demographics or locations. All brand mentions are automatically classified as urgent and routed to relevant team members.
Beyond figures and data, businesses can gain a better knowledge of customer feelings and opinions. Compare your brand’s image to that of your competitors to see how it changes over time. You may follow product releases, marketing campaigns, IPO filings, and other events at a precise point in time and compare them to previous occurrences.
Real-time sentiment analysis allows you to spot potential PR disasters and act quickly before they escalate into major concerns. Alternatively, identify positive comments and respond directly to them so that you can benefit from them.
Social Media Monitoring
In social media monitoring, sentiment analysis is used to get insights into how customers feel about certain topics, gain valuable market research, and spot urgent concerns in real time before they spiral out of hand.
Brands of all types and sizes engage in meaningful social media engagements with customers, leads, and even competitors. You can analyze client mood in real time and over time by monitoring these chats, allowing you to spot angry customers quickly and respond proactively.
In terms of volume, most marketing teams are already tuned into Internet mentions, interpreting increased talk as increased brand awareness. However, for deeper insights, firms must look beyond the metrics.
You may use sentiment analysis and text classification to automatically categorize incoming support requests by topic and urgency so that they are sent to the appropriate department and the most important ones are dealt with first.
Examine customer service encounters to check that your team is following proper procedures. Increase efficiency so that clients don’t have to wait for help. Reduce attrition rates; after all, keeping clients is easier than finding new ones.
How to Perform Sentiment Analysis?
Why not hire a company that can gather and integrate data, precisely analyze it for common themes and trends, and generate a report to provide the data in a high-quality and accessible way to make the process easier? For one-time or ongoing text analysis projects, our team provides a turnkey solution and can step in at any time to help with training, data analysis, advanced categorization, and more.
The following is only a partial list of the features Semeon Analytics offers:
- You may drag and drop data from any public or private source into Semeon’s feedback platform, including survey results, social media content, blogs, and support tickets.
- Semeon automatically separates the positive, negative, and neutral ideas extracted from your data using green, red, and gray color coding.
- Semeon saves you hours of time by extracting completely articulated concepts such as “Problem shipping to Canada,” rather than single-word phrases such as “Problem.”
We are data analysis experts with the visualization tools to help organizations turn static data into dynamic and informative visualizations, charts, and tables. To improve your data analysis and marketing skills, feel free to contact us!