Sentiment Analysis in AI is the process of using natural language processing, text analysis, and computational linguistics to identify and extract subjective information from textual data. This technology can be used to analyze customer feedback, social media posts, reviews, and other forms of text data to determine the overall sentiment of a particular message, product, or service. Sentiment Analysis in AI has numerous applications, including marketing, customer service, and brand management, and can provide valuable insights into the opinions and preferences of consumers.
What Is Sentiment Analysis?
Essentially, sentiment analysis is the process of identifying positive or negative sentiments in the text. The text can be a conversation or encounter between two parties or any other kind of written text. It uses data mining, natural language processing, artificial intelligence (AI), and machine learning (ML) approaches.
By using this, sentiment analysis can extract and classify users’ opinions on a business, product, person, service, event, or idea for different sentiments. Businesses employ it to analyze social media data for sentiment, test brand repute, and comprehend clientele.
Types of sentiment analysis
Sentiment analysis is primarily concerned with a text’s polarity. It can categorize a text as positive, negative, or neutral. However, it also extends beyond polarity to identify certain moods and emotions, such as anger, joy, or sadness. it can also detect, urgency and even intent.
Users can construct and customize their categories to match their sentiment analysis needs to be based on how they wish to interpret consumer comments and inquiries. Here are a few of the most well-liked varieties of sentiment analysis in the interim:
1. Fine-grained
You can carefully define the polarity of the text or interaction when using the fine-grained type of sentiment analysis. Polarity suggests emotions ranging from very positive or very negative to positive, negative, or neutral.
Under this area, the AI examines reviews and ratings from customers. For instance, using the scale of 1 to 10 suggests that a rating of 1-4 may show a negative mood whereas a rating of 5 to 10 indicates a favorable sentiment.
2. Aspect-based
The second type of sentiment analysis is aspect-based. It examines particular features that users discuss in relation to a good, service, or concept. Let’s take an example where a client reviews a laptop and says, “The webcam seems to go on and off at random times.”
In this instance, aspect-based analysis allows the laptop manufacturer to comprehend the customer’s “negative” comment regarding the laptop’s “webcam” component.
3. Emotion detection
Emotion detection sentiment analysis focuses on identifying emotions like happiness, sadness, fear, concern, and other kinds of feelings. It makes use of classifiers powered by machine learning as well as lexicons. Lexicons are a collection of words and idioms that denote particular emotions.
Since people have different ways of expressing their emotions, users prefer ML-based emotion identification over lexicons. For instance, “This phone is really nuts.” As it might elicit two distinct sentiments, such a review might throw the sentiment analysis model off. While the word “crazy” may describe it as one signifying dread or terror, one may be wholly positive.
Simply using lexicons may result in unreliable findings, but this issue can using ML-based detection can mitigate that issue.
4. Intent analysis
In order to focus their efforts and save time and money, firms must consider consumer intent as a crucial factor. By determining user intent, intent analysis assists in achieving this goal. User intent concerns the interest of a user when making a purchase or in simply exploring the website without making a decision,
When managing a business, organizations using intent analysis can locate a product’s intended buyers with the help of tailored ads. Furthermore, they can avoid expenditures, labor, and resources used for advertising by leaving those who do not plan to buy the goods alone.
Sentiment Analysis in AI: How does it work?
Natural language processing (NLP) and machine learning techniques enable sentiment analysis, also known as opinion mining, to automatically identify the emotional tone of online chats.
Depending on how much data you need to analyze and how precise your model has to be, you can use a variety of algorithms in sentiment analysis models
Rule-based Approaches
A rule-based system typically employs a set of rules that have been created by humans to help determine subjectivity, polarity, or the topic of an opinion. These guidelines may encompass different NLP methods created by computational linguists. One of these techniques is lexicons. Lexicons are collections of words used to convey the intention, feeling, and tone of the author.
The rule-based methodology uses predetermined lexicons to identify, categorize, and score particular keywords. Marketers rate the emotional impact of various terms by assigning sentiment scores to positive and negative lexicons.
The software searches for words from the lexicon to determine whether a sentence is good, negative, or neutral before calculating the sentiment score. To establish the overall emotional bearing, the software compares the with the sentiment bounds.
Rule-based systems are extremely unsophisticated because they don’t consider how words are combined in a sequence. However, these tools can add additional rules to handle new expressions and vocabularies and employ more sophisticated processing methods.
New regulations could also change earlier outcomes, and result in the system as a whole becoming exceedingly complicated. Rule-based systems will also need ongoing investments because they frequently need adjusting and upkeep.
Automatic Approaches
Contrary to rule-based systems, automatic solutions rely on machine learning techniques rather than manually constructed rules. A classification problem is typically used to represent a sentiment analysis task, where a classifier uses a text and outputs a category. These categories are usually positive, negative, or neutral.
What is sentiment analysis in AI used for?
Sentiment analysis for brand monitoring
One of the most well-documented uses of sentiment analysis is by allowing customers to get a complete 360-degree view of the brand, product, or company. Product evaluations and social media are widely accessible forms of media that can provide important details about the successes and failures of your company.
Sentiment analysis is another tool that businesses can use to gauge the impact of a new product, marketing initiative, or consumer reaction to recent corporate news on social media.
Sentiment analysis for customer service
Customer care representatives frequently employ sentiment analysis or intent analysis to categorize incoming customer emails into “urgent” or “not urgent” buckets based on the sentiment of the email, proactively detecting dissatisfied users.
The agent then prioritizes their time by starting with the users who have the most pressing problems. Understanding the sentiment and intent of a particular case becomes more crucial as machine learning automates customer support.
Sentiment analysis for market research and analysis
Business intelligence uses sentiment analysis to comprehend the irrational factors influencing consumer behavior. It allows leaders and executives to answer all kinds of questions. They gain insight into why consumers purchase a product, what they think of the user experience, and if customer care support matches their expectations.
Sentiment analysis is also useful in the fields of political science, sociology, and psychology. This is because it is able to examine patterns, ideological bias, opinions, and gauge reactions, among other things.
What are sentiment analysis use cases?
Companies utilize sentiment analysis to gather information and create practical solutions for a variety of situations. Here are the various ways how:
Improve customer service
To tailor responses based on the tone of the conversation, customer support professionals employ sentiment analysis technologies. Artificial intelligence (AI)-based chatbots with sentiment analysis capabilities can identify urgent issues and escalate them to the support team.
Brand monitoring
Businesses continuously keep an eye out for mentions of their brands in forums, blogs, news articles, and other digital areas.
Ongoing stories can inform a business’s public relations team thanks to sentiment analysis tools. The group can assess the general atmosphere to address grievances or seize upon promising trends.
Market research
A sentiment analysis system teaches firms what works and what doesn’t in order to improve their product offerings. Marketers can acquire deeper insights into particular product characteristics by examining comments on online review sites, survey results, and social media posts. They also share their findings with the product engineers, who then innovate in line with them.
Track campaign performance
To make sure that the response to their advertising campaign is as intended, marketers use sentiment analysis techniques. They monitor talks on social media platforms to make sure the tone is upbeat.
Based on real-time data analytics, marketers also use this kind of AI to adjust the campaign if the net sentiment is below expectations.
Top AI sentiment analysis software
1. Brand24
Sentiment analysis is one of the many tools available in Brand24. It is a media monitoring tool that you can utilize to your benefit. Web and social media monitoring is available from Brand24.
The application covers all significant blogs, forums, news websites, podcasts, and newsletters in addition to key social media networks.Brand24 uses its sentiment analysis algorithm to gather and process social media mentions posts.
2. Lexalytics
Lexalytics has the ability to process large amounts of text data, enable security to run the system behind your firewall, or even modify and configure your text analytics.
Three steps to describe how the text analytics platform functions in Lexalytics. Initially, they dissect phrases and sentences using text deconstruction and natural language processing to assess their semantics, grammar, etc.
The second step is when sentiment analysis, classification, name entity recognition, intention detection, and other techniques are used. In order to make historical and predictive analytics easier for the user, structured data and conclusions are finally transferred into their data visualization suite or business intelligence platforms.
3. Repustate
Repustate offers text analytics for organizations in 17 different languages using a sentiment analysis technology. Prior to performing the actual analysis, the tool activates a technique called part-of-speech tagging, which concerns the breakdown of text blocks into grammatical parts.
Once this step is complete, identifying the phrases that are the most intriguing in terms of sentiment analysis is much simpler. It’s important to note that the tool emphasizes a number of additional criteria, like lemmatization and prior polarity.
4. Clarabridge
The Customer Experience Management package from Clarabridge, which consists of CX Analytics and CX Social, includes a sentiment analysis tool.
They index the sentiment of the gathered content using an 11-point scale. While evaluating a piece of text, grammar, context, industry, and source are all taken into consideration.
This consumer feedback collection and sentiment analysis tool is ideal for identifying positive, negative, or neutral comments. To receive a price estimate for CX Analytics, you must request a demo. At the same time, you may sample CX Social for free for 14 days, which includes social media monitoring.
5. ParallelDots
ParrallelDots is an applied AI research group and the sentiment analysis tool is one of their services. ParallelDots has different categories: Products, APIs, and Plugins, which is where their sentiment analysis tool falls into.
Their sentiment analysis API uses Long Short Term Memory (LSTM) algorithms to classify a text blob’s sentiment into positive or negative. LSTMs model sentences as a chain of forget-remember decisions based on context. It distinguishes casual and formal language by training itself on social media and news data.
FAQs
What is sentiment analysis?
Sentiment analysis enables the analysis of a text. Its underlying sentiments are then deciphered under this process. Algorithms can categorize utterances into positive, negative, and neutral categories using machine learning and text analytics.
Companies and businesses frequently utilize this procedure, also known as “opinion mining,” as a technique for social media monitoring to handle enormous amounts of data and obtain consumer insights to understand the attitudes of customers and competitors.
What is the relation between sentiment analysis, natural language processing, and machine learning?
Computers utilize a method called natural language processing, a subset of artificial intelligence that also includes machine learning, to comprehend, interpret, and manipulate human speech. NLP uses a variety of algorithms to decipher, analyze, and interpret human language in order to extract meaning from the enormous amount of unstructured data.
Sentiment analysis is one of NLP’s most often utilized applications. It identifies and extracts opinions from language, whether it be spoken or written. It’s sometimes referred to as “opinion mining.” This analysis aids in determining the statement’s polarity, emotional overtone, and subject.
How is machine learning used in sentiment analysis?
Classification is a concept found in machine learning and is a type of Supervised Learning. Simply put, sentiment analysis is the use of classification. We each have pre-established emotions, such as positive and negative ones. Sentiment analysis categorizes the text using a machine-learning model. These categories follow the aforementioned emotions that are either positive or negative.
How long does it take to implement a simple sentiment analysis algorithm?
One of the simplest machine learning issues is sentiment analysis. Specific keywords can typically be used to assess sentiment, making it quite easy and quick to program.
