Sentiment analysis examines the feelings conveyed within a written piece. It is frequently used to examine product reviews, survey results, and consumer feedback. Sentiment analysis has applications in customer experience, reputation management, and social media monitoring, to name a few. For instance, you can get insightful input on your pricing or product features by examining thousands of product reviews.
We’ll delve deeply into the operation of sentiment analysis in this extensive guide. We will examine the principal business applications of sentiment analysis. We’ll also examine this analysis’s present shortcomings and challenges.
Sentiment analysis: What is it?
To ascertain whether a particular text contains neutral, positive, or negative emotions, sentiment analysis is utilized. It’s a type of text analytics that makes use of machine learning and natural language processing (NLP). Other names for sentiment analysis include “opinion mining” and “emotion artificial intelligence.”
Rating Sentiment
Polarity categorization is one of the main components of sentiment analysis. A sentence, phrase, or word’s overall sentiment is referred to as its polarity. A “sentiment score” is a numerical rating that represents this polarity. This score, for instance, maybe a figure between -100 and 100, where 0 would indicate a neutral mood. It is possible to compute this score for a single phrase or the full text.
Sentiment Analysis with Fine Gaps
Sentiment analysis can be as detailed as needed for a given use case. There are more categories than just “positive,” “neutral,” and “negative.” For instance, you could decide to employ five categories.
Sorting customer evaluations with one star as “very negative” is a simple approach to achieve this. Reviews with five stars are considered to be “very positive.”
Additionally, you can further hone the sentiment into particular feelings. Positive feelings can be further developed into other emotions, such as happiness, excitement, impressing, trusting, and so forth. Emotion analysis, which we discussed in one of our earlier posts, is usually used for this.
Sentiment analysis based on aspects (ABSA)
When sentiment analysis is connected to a certain quality or aspect that is mentioned in a text, it is most helpful. Aspect-based Sentiment Analysis, or ABSA, is the process of identifying these characteristics or traits and their sentiment. These elements are referred to as “themes” at Thematic. For instance, you could be interested in CPU speed when reading product reviews for laptops. When a sentence discusses processor speed, an aspect-based method can be used to identify whether it is favorable, negative, or neutral.
ABSA for on-the-spot observation
AI’s learning domain teaches machines how to carry out tasks by examining data. Algorithms for machine learning are designed to find patterns in data. Algorithms for machine learning can be trained to accurately interpret any new text. Because of this, emotion on processing speed can be measured even in situations when users use slightly different terms. For instance, “slow to load” or “speed issues” could both be seen as factors that negatively impact the laptop’s “processor speed.”
Businesses employ aspect-based sentiment analysis using machine learning-based solutions to internal customer communication channels, social media, review sites, and online communities. Data visualizations can be used to examine the ABSA findings and pinpoint areas that want improvement. Sentiment generally, sentiment over time, and sentiment by rating for a specific dataset are examples of these kinds of visualizations.
Machine Learning and ABSA
Real-time monitoring can greatly benefit from aspect-based sentiment analysis. Companies are able to quickly determine what problems consumers are bringing up in reviews or on social media. This can enhance customer satisfaction and expedite response times.
What Makes Sentiment Analysis Vital?
The main objectives of any business are to increase sales and retain consumers. Apex Global Learning’s research indicates that an increase of five to nine percent occurs in income for each star added to an online review. The revenue difference between companies with three-star and five-star ratings is eighteen percent.
Sentiment analysis is a useful tool for understanding consumer attitudes toward your product or brand on a large scale. The sheer volume of data makes it difficult to accomplish this manually most of the time. Businesses can now more easily obtain deeper insights into their text data thanks to specialized SaaS technologies. This might contain anything from social media posts and employee surveys to feedback from customers. Important business decisions can be informed by the sentiment data obtained from many sources.
Advantages Of Emotional Analysis
Let’s examine the main advantages of sentiment analysis (2024) in more detail.
More Reliable
Eliminates human prejudice by using a consistent analysis
Emotions can be very individualized. Humans communicate meaning through language, tone, and context. It is our unconscious prejudices and personal experiences that shape our understanding of that meaning. Let’s examine a customer evaluation of a recent SaaS product in order to investigate this further:
“Does the job, but it costs a lot!”
This statement contains both good and negative sentiment. Price and negative mood are related. Positive feelings are associated with the product’s functionality. However, what is the sentence’s general meaning?
This is where bias and mistakes made by humans can occur. Given that the reviewer emphasizes functionality in a good manner, human analysts might interpret this line as generally positive. However, they might highlight and label the unfavorable remark about the pricing. This is but one illustration of how subjectivity affects how sentiment is perceived.
Consistent criteria are applied by sentiment analysis methods to produce more precise findings. A machine learning model, for instance, can be trained to identify that two characteristics have two distinct feelings. It would record the specifics while maintaining a neutral overall sentiment average.
Handles data in bulk: Greater strength
Businesses can make sense of massive amounts of unstructured data with the aid of sentiment analysis. When working with text, even fifty instances may seem like a lot of data. Particularly when handling user opinions on social media or in product reviews.
Consider a business that has just introduced a new product. Instead than going through hundreds of reviews, the business can enter the information into a feedback management program. Its model for sentiment analysis will categorize incoming feedback based on sentiment. The business may respond appropriately and learn more quickly from customers’ opinions about their new product. They can identify areas for improvement as well as qualities that clients find appealing.
Businesses can also learn from this kind of analysis how many consumers have a particular opinion of their product. The quantity of employees and the general mood regarding, instance, “online documentation,” can influence a company’s priorities. To stay competitive and prevent client attrition, businesses should, for instance, concentrate on producing better documentation.
Mechanization! Conserve time.
Text containing hundreds of megabytes can be analyzed in minutes using sentiment analysis algorithms. Rather than devoting your time to tedious spreadsheet data analysis, you can now focus on more worthwhile endeavors. You could, for instance, confirm the following insight: Is it worthwhile to take action? The business context is also optional. Is there a seasonal problem, if there is one? Have they been observed elsewhere in the company? Sentiment analysis is ultimately just a signal. However, you will have time to formulate the best plan if you receive this signal quickly and with little effort.
Algorithms and techniques for sentiment analysis are always improving. Better and more varied training data is fed to them, making them better. Additionally, scientists create new algorithms to make better use of this data. At Thematic, we track your outcomes and evaluate mistakes. If necessary, we supplement the deficient areas with more precise training data. Consequently, sentiment analysis is improving in precision and yielding more focused insights.
Move more quickly: Instantaneous evaluation and understanding
Machine learning is used to automate sentiment analysis. This implies that real-time insights are available to enterprises. This can be quite useful in determining which problems require immediate attention. For instance, a bad story that is spreading on social media can be swiftly addressed and picked up in real time. Other customers may experience the same issue if one complains about one about their account. Businesses can avoid negative experiences by promptly notifying the appropriate teams to address this problem.
Sentiment Analysis’s Commercial Uses
Sentiment analysis is helpful in interpreting qualitative data that businesses routinely collect via a variety of methods. Now let’s examine a few of the most popular corporate uses.
VOC (Voice of the Customer) Programs
It’s critical to know how your customers feel about your items or brand. You can use this information to enhance the client experience or to find and address issues with your goods or services. As a business, you must gather information from clients regarding their perceptions of and needs for your goods and services in order to do this. We refer to this input as Voice of the Customer (VoC) feedback.
Surveys that measure Net Promoter Score (NPS) are frequently used to gauge customer satisfaction. “How likely are you to recommend us to a friend?” is a common question asked of customers.Typically, the feedback is given as a number between 1 and 10. “Promoters” are customers who give a response a score of 10. They are most likely to tell a friend or relative about the company. A high NPS indicates increased client retention. Having more promoters also results in improved word-of-mouth marketing. It follows that your expenditure on paid customer acquisition should be reduced.
NPS surveys have the disadvantage of not providing you with much insight into the true reasons behind your customers’ opinions. The NPS rating questions are supplemented by open-ended inquiries. They convey the reasons behind consumers’ propensity to or reluctance to suggest goods and services. Sentiment analysis makes this text into an NPS generator.
NPS is merely one kind of VoC survey. Any metric that matters to you, such as customer satisfaction, customer effort score, etc., follows the same principle. Which metric is employed actually doesn’t matter all that much. What matters more is the reason behind the metric’s fluctuations.
A top-notch VOC program involves keeping an ear out for client comments via all available channels. Even for a mid-size B2B company, you can see how rapidly it may mushroom to hundreds of thousands of pieces of feedback. Understanding this data requires sentiment analysis.
Lastly, businesses can also act fast to resolve critical issues and identify clients who are experiencing extremely unfavorable experiences. Monitoring consumer opinion over time can assist you in spotting new concerns and taking action before they worsen.
Experience with Customer Service
A company’s ability to provide excellent customer service can make or break it. Clients want assurance that their question will be answered professionally, promptly, and efficiently. Businesses may improve and expedite their customer care experience with the aid of sentiment analysis.
Customer support discussions can benefit from the application of both sentiment analysis and text analysis. Conversations can be automatically ranked by subject and urgency using machine learning techniques. As an illustration, suppose you have a community where members report technical problems. An emotion analysis system can identify posts that elicit a lot of frustration from users. An internal expert can be assigned priority for certain questions. Answers to common queries can be obtained from other community members.
As you can see, by directing questions to the appropriate individuals, sentiment analysis can decrease processing times and boost efficiency. Customers ultimately receive better service, and you can lower your churn rates.
Analysis of Brand Sentiment
A brand’s reputation can affect sales, customer retention rates, and the likelihood that customers will refer others to it. The 2004 documentary “Super Size” followed director Morgan Spurlock for thirty days straight as he only consumed McDonald’s cuisine. The subsequent media frenzy, together with additional unfavorable press, saw the company’s UK profits plunging to their lowest points in thirty years. In response, the business started a PR campaign to enhance their reputation.
Brands can track their customers’ sentiments about them with the use of sentiment analysis. To monitor the reputation of their brand, they might examine social media platforms, forums, and communities. Alternatively, they may carry out surveys to find out what concerns their clients are most passionate about.
In order to develop a long-term awareness of their brand image, companies also monitor mentions of their brand, product names, and competitors. This aids businesses in determining how a new product launch or PR effort has affected the perception of their brand as a whole.
Sentiment analysis on social media
Social networking is an effective tool for connecting with and gaining new clients. Positive social media posts and reviews from clients entice additional clients to make purchases from your business. However, the opposite is also true. Unfavorable remarks or evaluations on social media can be really expensive for your company.
Convergys Corp. research shows that a bad review on Facebook, Twitter, or YouTube can cost a business roughly thirty clients. Significant financial losses might also result from negative social media posts about a business. Elon Musk’s 2020 tweet in which he said the price of Tesla stock was too high is one notable example.
Within hours, the widely shared tweet reduced Tesla’s valuation by $14 billion. Real-time sentiment analysis can be used to spot these kinds of problems before they get out of hand. Companies can then act swiftly to minimize financial loss and repair any harm to their reputation as a brand.
Market analysis
Businesses can use sentiment analysis to investigate new markets, evaluate competitors, and spot developing trends. Businesses might want to examine product or service reviews from rival companies. By using sentiment analysis on this data, businesses can find out what aspects of their rivals’ products appeal to or annoy customers. These observations may help you obtain a competitive advantage. Sentiment analysis, for instance, can show that consumers of rival companies are dissatisfied with their laptops’ short battery lives. The business could then emphasize in its marketing materials how much longer their batteries last.
In order to identify fresh prospects, sentiment analysis could also be used to analyze market reports and business periodicals. An examination of real estate industry statistics, for instance, may indicate that a certain location is getting more and more favorable press. This data may indicate that professionals in the field believe there is a strong investment potential here. Then, by making investments before the rest of the market, these insights could be leveraged to obtain an early advantage.
Rule-based Sentiment Analysis’s drawbacks
Because rule-based methods do not take the language as a whole into account, they are limited. Because human language is so complicated, it might be simple to overlook intricate analogies and negations. In order to maximize performance, rule-based systems also frequently need to be updated on a regular basis.
Automated Sentiment Analysis or Machine Learning
Machine learning (ML) techniques are necessary for automated sentiment analysis. Here, an ML algorithm has been trained to identify sentiment based on the words themselves as well as their arrangement. The caliber of the algorithm and the training data set determine how well this strategy works.
Additionally, there are hybrid sentiment algorithms that integrate rule-based and machine-learning techniques. Even though they are far more difficult to construct, they can provide higher precision.
Step 1: Extraction of Features
The text must be formatted such that a computer can read it before the model can classify it. Similar to rule-based techniques, tokenization, lemmatization, and stopword removal can be a component of this process. Additionally, text is converted into numbers by a procedure known as vectorization. “Features” refers to these numerical depictions. Frequently, this is accomplished by applying the bag-of-words or bag-of-n-grams techniques. These vectorize text based on the frequency of word occurrences.
Text vectorization can now be done in novel ways thanks to deep learning. The word2vec algorithm, which makes use of a neural network model, is one example. Word associations can be taught to the neural network by exposing it to vast amounts of text. Word2vec uses a vector, or a list of numbers, to represent each unique word. This method has the benefit of giving words with comparable meanings numerical representations. This may contribute to sentiment analysis’s increased accuracy.
Step 2: Instruction and Forecasting
The algorithm is then fed a training set that has been sentiment-labeled. Subsequently, the model gains the ability to correlate incoming data with the most suitable label. Pairs of characteristics, or numerical representations of text, and the matching positive, negative, or neutral label, for instance, would be included in this input data. Either manually generated or generated from the reviews themselves can be used to build the training data.
Step 3: Forecasts
When compared to rule-based methods, ML sentiment analysis excels at the last stage. The model is fed fresh text. Using the model it learned from the training data, the model then predicts labels (also known as classes or tags) for this unseen data. As a result, the sentiment of the data can be classified as good, negative, or neutral. This removes the requirement for rule-based sentiment analysis’s predefined lexicon.
Alphabets for classification
The sentiment of a given text can be predicted using classification techniques. The steps above provide more details on how they are trained with pre-labeled training data. Naive Bayes, Logistic Regression, Support Vector Machines, Linear Regression, and Deep Learning are frequently used classification models. Let’s take a closer look at these algorithms.
Naive Bayes: The Bayes Theorem is the foundation for this kind of classification. These algorithms determine the likelihood of a label for a given text since they are probabilistic. The highest likelihood label is then applied to the text. The term “naive” describes the underlying presumption that every feature is autonomous. Every syllable contributes equally and independently to the final result. This assumption can make the algorithm function successfully even in situations when the data is sparse or incorrectly labeled.
An approach for classification called logistic regression uses independent factors to predict a binary result. The sigmoid function is used, and the result is a probability between 0 and 1. Positive and negative words and phrases can be distinguished from one another. “Very slow processing speed,” for instance, might be categorized as 0 or negative.
A linear regression technique uses words and phrases (X input) to predict polarity (Y output). Finding a linear model or line that can be used to predict sentiment (Y) is the goal. Reducing the error can help the model become more accurate.
Example of simple linear regression.
Deep Learning: several layers of processing are carried out by an artificial neural network. A broad range of algorithms known as “deep learning” use abstractions and associations to mimic the way the human brain learns. Compared to conventional classification algorithms, deep learning offers a number of advantages. These neural networks are capable of comprehending the writer’s mood as well as the context.
In-depth Learning & Emotion Recognition
Given that deep learning produces the most accurate sentiment analysis, it is worthwhile to investigate this method further. Traditional machine learning techniques, which necessitate manual labor to define categorization features, dominated the area until recently. They frequently overlook the significance of word order as well. NLP has changed as a result of deep learning and artificial neural networks.
The architecture and operation of the human brain served as an inspiration for deep learning algorithms. The accuracy and effectiveness of sentiment analysis increased as a result of this method. The neural network in deep learning has the ability to self-correct when it makes a mistake. Errors in traditional machine learning require human involvement to correct.
Extended Short-Term Memory
Long Short-Term Memory, or LSTM, is a key Deep Learning technique. This method saves pertinent information while reading a text in a sequential fashion.
The three components of the LSTM are referred to as “gates”:
The Forget Gate determines whether or not prior data should be remembered in the first section. It can be overlooked if it has no bearing on the task at hand.
- Input Gate: The cell attempts to extract new information from the fresh data in the second section.
- Output Gate: This last section is when the cell sends the new data to the subsequent timestamp.
It is helpful for sentiment analysis that the LSTM contains cells that regulate which information is recalled or ignored. For sentiment analysis to be accurate, negativity is essential. Any person can see, for instance, that “great” and “not great” are very different from one another. An LSTM can determine which words need to be negated and can also learn to value this distinction. Moreover, the LSTM can deduce grammar rules by reading a lot of material.
Transformer prototypes
LSTMs are not without restrictions, particularly when dealing with lengthy sentences. The content of distant words is frequently forgotten by the model. Additionally, each word in the statement must be understood.
Using a transformer is an additional option. Each component of the data has its significance weighted differently by this approach. The transformer does not have to process the sentence’s beginning before its conclusion, in contrast to an LTSM. Rather, it pinpoints the context that gives every word its meaning. One term for this is an attention mechanism. Since transformers are more adept at analyzing longer sentences than LTSMs, they have now largely superseded them.
Trained models beforehand:
With pre-trained models, sentiment analysis can be begun immediately. For businesses without the means to acquire huge datasets or train intricate models, it’s an excellent option.
What Are Sentiment Analysis’s Present Difficulties?
Sentiment analysis determines a text’s neutrality or positivity by utilizing natural language processing (NLP) and machine learning. Rule-based and automated sentiment analysis are the two basic methods.
Subjectivity:
- Both subjective and objective texts are possible.
Look at these sentences as an illustration:
It is obvious from the first statement that it is subjective, even if most people would interpret it as good. Since it is objective, the second sentence might be categorized as neutral. In this context, “good” is seen as more arbitrary than “small.”
The problem here is that subjectivity is a problem for machines all the time. Consider a review of a product that states, “The software works great, but there is no way that justifies the massive price tag.” The initial part of the statement is positive in this instance. However, the second half refutes it, stating that it is excessively costly. The sentence has a bad overall tone.
Algorithms can be assisted in correctly classifying sentiment by using large training datasets that contain a high number of examples of subjectivity. Because deep learning is more adept at accounting for tone and context, it may also be more accurate in this situation.
Settings:
Words with strong opinions can become less strong depending on the situation. For machines to accurately categorize a text, they must first learn about context.
For instance, the following responses may be given to the question “What did you like about our product”:
- “Adaptability”
- “Features”
A positive response would be assigned to the first response. Although the second response is equally affirmative, it is unclear on its own. The inquiry would have the opposite polarity if we asked, “What did you not like?” Sometimes the context is provided by the rating rather than the question.
Preprocessing or postprocessing the data to obtain the required context is the answer to this problem. This can be a difficult and drawn-out procedure.
Sarcasm and Irony?
Machine learning systems might face significant hurdles when dealing with humor and sarcasm! Consider the following actual instance of a letter of complaint that traveler Arthur Hicks wrote to LIAT Caribbean Airlines:
People use good language to describe bad events with sarcasm and irony. As robots don’t know what passengers anticipate from airlines, it can be difficult for them to interpret this sentiment. Words like “considerate” and “magnificent” in the example above would be categorized as having a positive sentiment. However, it is evident to a human being that the general attitude is unfavorable.
Fortunately, sarcasm is used in just a very small percentage of reviews when it comes to businesses.
The Accuracy Of Human Annotators Is Limited
As previously said, even humans have difficulty accurately identifying sentiment. An inter-annotator agreement, also known as consistency, can be used to gauge how well two or more human annotators agree on an annotation. Since machines pick up knowledge from training data, these possible mistakes may affect how well a machine-learning model performs sentiment analysis.
Thematic’s sentiment analysis accurately predicts sentiment in text data 96% of the time, according to a recent test. However, we also had a lengthy conversation on what accuracy actually means and why one should always take reports of accuracy with a grain of salt.
Nevertheless, regarding aspect-based sentiment analysis (ABSA), as previously mentioned, we conducted a study in which we contrasted aspects found by four individuals with aspects found by Thematic. We discovered that Thematic generally agrees with individuals more than they do with one another!
How Sentiment Analysis Can Be Started
It can be scary to begin using sentiment analysis. Fortunately, automated SaaS sentiment analysis solutions and a wealth of internet materials are available to assist you. Alternatively, you could decide to use open-source technologies to create your own solution.
Selecting A Method for Sentiment Analysis
Should you purchase pre-made software or create your own? The answer most likely relies on your financial situation and available time. Building internally is typically more expensive. Let’s examine the specifics of creating your own solution or purchasing an already-existing SaaS offering.
Constructing Your Own
Developing your own sentiment analysis program might be a difficult and drawn-out procedure. The actions needed to construct this kind of tool are:
Investigate:
Finding the machine learning solutions that work best for your company is the first step. It will also be necessary to think about the programming language to utilize.
Construct:
You have two options: either create the algorithms from scratch or, more likely, employ a pre-made model.
Recognizing Best Models for Sentiment Analysis: Training Models:
A training set with sentiment labels is fed into the algorithm. Subsequently, the model gains the ability to correlate incoming data with the most suitable labels. Because the training data must be generated, labeled, or vetted, this process can take some time. Integrate: Construct an API or combine the model by hand with your current tools. If your product is used by coworkers who are not technical, you might also need to create an interface that is easy to use.
Group instruction:
Teams that aren’t technical in particular could need thorough onboarding instructions to use the product. It can be necessary to write internal training guides. Launch: Using your tool inside your company is the last step. It might take frequent observation and adjustment to maximize performance.
Advantages:
You can modify the tool to precisely match your business needs.
Cons:
It takes time to develop your own sentiment analysis program. A simple sentiment analysis system should be built in no less than four to six months. It could be necessary to reassign or hire a group of programmers and data engineers. It can take longer to create unique software than you anticipated. If the team has unforeseen issues, it is easy to miss deadlines. This may result in a sharp rise in expenses. The tool will require monitoring and updates after it is constructed. Because it’s a custom solution, only the IT team who built it will understand how it functions as a whole.
Sentiment Analysis in Python
Python is a widely used programming language in sentiment analysis applications. One benefit of using Python is the abundance of freely accessible open-source libraries. Creating your own sentiment analysis solution is made simpler by these.
Purchasing SaaS Software
Numerous pre-built sentiment analysis tools, such as Thematic, are available to help you save time, money, and mental strain.
The benefits and drawbacks of utilizing a SaaS solution for sentiment analysis will be discussed.
Advantages:
SaaS solutions such as Thematic let you immediately begin using sentiment analysis. Sentiment analysis models that have already been trained on client comments are immediately useful.
Coding is not required. Because of this, SaaS solutions are perfect for companies without in-house data scientists or software engineers.
Compared to creating a bespoke sentiment analysis solution from scratch, costs are far lower.
Secure and easy data transfer is made possible by one-click interfaces with feedback-gathering tools and APIs.
full customer support available to assist you in making the most of the product.
Cons:
On the market, there are numerous options for sentiment analysis. Opting for the best option for your business might be challenging.
Employing Theme to Gain Insights from Sentiment Analysis with Power
Purchasing a SaaS solution with sentiment analysis integrated is often the most cost-effective choice for several firms. Sentiment analysis on customer reviews or other text formats is made simple with Thematic, a fantastic tool.
Thematic makes use of machine learning-trained sentiment analysis algorithms on massive amounts of data. Thematic is distinct in that it integrates feelings with themes found through the thematic analysis procedure.
Sentiment analysis versus theme analysis
Let’s take a brief look at these two analysis types before getting into the advantages of integrating sentiment and theme analysis.
Analysis by Theme
Finding recurring themes in a text is the technique of theme analysis. Regardless of the words and phrases used to express it, a theme sums up the main ideas of the writing. For instance, one individual might remark, “The food was yummy,” whereas another might remark, “The dishes were delicious.” It’s the same theme in both situations. We may refer to it as “tasty food.”
To automate this operation, Artificial Intelligence researchers developed Natural Language Understanding algorithms. These algorithms power thematic software. Read our blog post to find out more about how it functions.
What role does Sentiment Analysis play here?
Aspect Based Sentiment Analysis, or ABSA, was previously discussed. Themes can represent both the aspect and its sentiment. Furthermore, Thematic locates the appropriate sentiment for each theme that is stated in the text.
How to Combine Thematic Analysis And Sentiment Analysis
Let’s go over how to use Thematic’s sentiment and thematic analysis to extract additional value from your textual data.
Step 1: Provide Your Information
Uploading your unstructured data to a feedback analytics platform like Thematic is the initial step. This could include comments left on online surveys, discussions in chat rooms, or mentions on social media. Connecting all of your channels is incredibly simple with Thematic’s extensive selection of one-click integrations. Qualtrics, Trustpilot, Amazon, Facebook, Twitter, Intercom, Tripadvisor, and numerous other platforms are among them. After that, Thematic automatically cleans and gets your data ready for analysis.
Step 2: Evaluation
Analysis by Theme You can then use thematic analysis to find themes in your unstructured data. Thematic AI classifies themes into two taxonomies. There will be main themes and associated sub-themes for any given text. One central theme can be “staff behavior,” for instance. “Friendly crew” could serve as a subtheme. This makes it simple for you to determine what topics your clients are discussing, say, in their reviews or survey responses.
Sentiment Analysis
Sentiment analysis enables you to comprehend the emotion underlying a theme by building upon thematic analysis. Sentiment analysis assigns a positive, neutral, or negative sentiment score to each word or theme.
The subject “print boarding passes” has been chosen in the Thematic dashboard of the sample above. An overview of the attitude related to this theme across your textual data is provided below. 61.2% of theme appearances are categorized as unfavorable, reflecting the general negative sentiment about this subject. Additionally, you can see that 0.4% of customer evaluations have this topic.
You can also choose to sort your topics according to sentiment. This makes it possible for you to rapidly pinpoint the aspects of your company where clients are dissatisfied. These insights can then be used to inform and enhance your business strategy.
These two forms of analysis together can be quite effective. It enables you to comprehend the opinions of your clients regarding specific facets of your offerings, services, or business.
Step 3: Metrics and Sentiment Analysis
Metrics like NPS and customer churn can also be better understood by combining sentiment and thematic analysis.
This Thematic dashboard sample shows how consumer sentiment is tracked over time by theme. As you can see, “bad update” was the main cause of the quarter’s dismal outcomes. As a result, stakeholders may quickly and easily comprehend what is influencing important business measures.
You can also utilize our Customer Goodwill metric with Thematic. Customer sentiment across all of your uploaded data is summed up in this score. It lets you have a general idea of how your clients feel about your business at all times.
You can observe the general sentiment over a number of channels in the example below. Each of these outlets adds to the 70-point Customer Goodwill score.
Step 4: AI & Human
You can also manually modify the analysis using Thematic’s platform. More accuracy and relevance are ensured when a human analyst works in tandem with artificial intelligence.
For instance, you could want to go through each theme and remove any that aren’t necessary. Additionally, you can build new themes, combine existing themes, and transition between main themes and sub-themes.
Step 5: Monitoring in Real Time
Ongoing real-time monitoring is the process’s last phase. This can assist you in keeping up with new trends and quickly spotting any PR emergencies or product problems before they get out of hand.
You can see how sentiment has changed over time for the topic “chat in landscape mode” in the example above. The graphic unequivocally demonstrates that, over time, a growing number of consumers have mentioned this theme negatively. Customer feedback on the right suggests that this is a newly discovered problem associated with a recent update. With the use of this data, the company can act swiftly to address the issue and reduce potential client attrition.
Books on Sentiment Analysis
There are some excellent publications available for individuals who want to grasp sentiment analysis in great detail. “Sentiment Analysis and Opinion Mining” by Bing Liu is a classic. Liu is regarded as a pioneer in machine learning thought leadership. His book does an excellent job of presenting sentiment analysis in a technical yet understandable manner.
“Deep Learning-Based Approaches for Sentiment Analysis” is a fantastic resource if you’d like to learn more about deep learning for sentiment analysis. It was released in 2020 and offers information on the most recent developments and trends in sentiment analysis deep learning.
“Sentiment Analysis in Social Networks” may be of particular interest to those with a social media bent. Liu wrote this specialized book with a number of other ML specialists. It examines statistical methods, huge data, and natural language processing.
Research papers on sentiment analysis
There is a steady stream of new research publications and an ever-evolving discipline of sentiment analysis. For individuals who wish to go more into particular subtopics, the following selection of recent publications is provided:
Bing Liu’s article “Sentiment Analysis and Subjectivity”
Carlos Iglesias and Antonio Moreno’s “Sentiment Analysis for Social Media”
“Agarwal et al.’s Sentiment Analysis of Twitter Data”
Shanshan Yi and Xiaofang Liu’s paper, “Machine learning based customer sentiment analysis for recommending shoppers, shops based on customers’ review,”
Reem Bayari, Said Salloum, and Arwa A. Al Shamsi, “Sentiment Analysis in English Texts.”
Training in Sentiment Analysis
To learn how to use NLP for sentiment analysis, there are a ton of online materials available. Here are some options to get you going:
Check out the “Sentiment Analysis, Beginner to Expert” course on Udemy for a fantastic introduction to the field.
A helpful course on “Natural Language Processing (NLP) in Python” is available on Udemy as well. This covers writing your own Python sentiment analysis code.
Buildbypython has created a helpful video series on sentiment analysis with natural language processing on YouTube.
Stanford Online is a good resource for those who prefer a more scholarly approach. Some of their lectures, including this one on sentiment analysis, are available on YouTube.
Datasets for Sentiment Analysis
If you don’t already have your own data, you might require access to appropriate datasets to begin sentiment analysis. Here are a few publicly accessible datasets that you may use to test out sentiment analysis:
- Product reviews on Amazon: this collection includes millions of fastText reviews from Amazon.com.
- Reddit comments: from 2009 to 2019, Reddit users’ opinions about Bitcoin are the subject of this fascinating dataset.
- Thousands of distinct hotel reviews can be found in this dataset of Booking.com reviews.
Lists of words that have already been pre-labeled with sentiment or sentiment analysis lexicons are required for those considering a rule-based approach. Here are a few practical choices:
Positive and negative sentiment lexicons for 81 distinct languages are included in “Sentiment Lexicons for 81 Languages.”
Sentiment word classifications are included in the “Loughran-McDonald Master Dictionary.”
The helpful tool “Emoji Sentiment Ranking v1.0” examines the sentiment of well-known emoticons. That said, you might be thinking,
Does Seo matter anymore?
Yes, SEO still matters in today’s digital landscape, and the simple, direct answer to the question of “does SEO matter anymore” is that it’s not going anywhere, anytime soon. While search engines continue to evolve and prioritize user experience, SEO techniques are still essential for improving a website’s visibility and ranking. By optimizing content with relevant keywords, improving site speed, and building quality backlinks, businesses can still benefit from a strong SEO strategy to drive organic traffic and increase online visibility.
Watch Digital Marketing Trends:
SEO matters and not only that, but you must also strategically place your ideas that will bring in the most amount of people to your page, blog, or website-related content in any field. You have to keep in view the ongoing trends and especially watch digital marketing trends to construct your SEO such that it bends the ideas used in these trends and uses them as its own in an original way. There are many free AI SEO tools that are ready to do what you will spend a lot of time doing manually, quickly, and without any hustle.
Conclusion:
Sentiment analysis determines a text’s neutrality or positivity by utilizing natural language processing (NLP) and machine learning. Rule-based and automated sentiment analysis are the two basic methods.
With any luck, this guide has provided you with a solid understanding of sentiment analysis and its applications in business. Applications for sentiment analysis include market research, HR, and brand monitoring. It’s assisting businesses in becoming more competitive, getting deeper insights, and understanding their clients better.
The discipline of sentiment analysis is likewise one that is rapidly expanding and changing. For this reason, it’s critical to keep up with the most recent developments. Using a platform like Thematic, which is always being updated and enhanced, is an additional choice. You can click this link to schedule a customized guided trial and learn more about how Thematic operates.