Sentiment analysis is used to determine whether a given text contains negative, positive, or neutral emotions. It’s a form of text analytics that uses natural language processing (NLP) and machine learning. Sentiment analysis is also known as “opinion mining” or “emotion artificial intelligence”.
A key aspect of sentiment analysis is polarity classification. Polarity refers to the overall sentiment conveyed by a particular text, phrase or word. This polarity can be expressed as a numerical rating known as a “sentiment score”. For example, this score can be a number between -100 and 100 with 0 representing neutral sentiment. This score could be calculated for an entire text or just for an individual phrase.
Sentiment scoring can be as fine-grained as required for a specific use case. Categories can expand beyond just “positive”, “neutral” and “negative”. For example, you may choose to use five categories
One easy way to do this with customer reviews is to rank 1-star reviews as “very negative”. 5-star reviews would be ranked as “very positive”.
You can also refine the sentiment further into specific emotions. For example, positive sentiment can be further refined into happy, excited, impressed, trusting and so on. This is typically done using emotion analysis, which we’ve covered in one of our previous articles.
Sentiment analysis is most useful, when it’s tied to a specific attribute or a feature described in text. The process of discovery of these attributes or features and their sentiment is called Aspect-based Sentiment Analysis, or ABSA. Here at Thematic we call these aspects “themes”. For example, for product reviews of a laptop you might be interested in processor speed. An aspect-based algorithm can be used to determine whether a sentence is negative, positive or neutral when it talks about processor speed.
Learning is an area of AI that teaches computers to perform tasks by looking at data. Machine Learning algorithms are programmed to discover patterns in data. Machine learning algorithms can be trained to analyze any new text with a high degree of accuracy. This makes it possible to measure the sentiment on processor speed even when people use slightly different words. For example, “slow to load” or “speed issues” which would both contribute to a negative sentiment for the “processor speed” aspect of the laptop.
Companies use Machine Learning based solutions to apply aspect-based sentiment analysis across their social media, review sites, online communities and internal customer communication channels. The results of the ABSA can then be explored in data visualizations to identify areas for improvement. These visualizations could include overall sentiment, sentiment over time, and sentiment by rating for a particular dataset.
Aspect-based sentiment analysis can be especially useful for real-time monitoring. Businesses can immediately identify issues that customers are reporting on social media or in reviews. This can help speed up response times and improve their customer experience.
Improving sales and retaining customers are core business goals. According to research by Apex Global Learning, every additional star in an online review leads to a 5-9% revenue bump. There’s an 18% difference in revenue between businesses rated as three-star and five-star ratings.
Sentiment analysis can help you understand how people feel about your brand or product at scale. This is often not possible to do manually simply because there is too much data. Specialized SaaS tools have made it easier for businesses to gain deeper insights into their text data. This could include everything from customer reviews to employee surveys and social media posts. The sentiment data from these sources can be used to inform key business decisions.
Let’s dig deeper into the key benefits of sentiment analysis.
Sentiment can be highly subjective. As humans we use tone, context, and language to convey meaning. How we understand that meaning depends on our own experiences and unconscious biases. To explore this further, let’s look at a customer review about a new SaaS product:
“Gets the job done, but it’s not cheap!”
There is both negative and positive sentiment in this sentence. Negative sentiment is linked to the price. Positive sentiment is linked to the functionality of the product. But what’s the overall sentiment of the sentence?
This is where human bias and error can creep in. Human analysts might regard this sentence as positive overall since the reviewer mentions functionality in a positive sentiment. On the other hand, they may focus on the negative comment on price and tag it as negative. This is just one example of how subjectivity can influence sentiment perception.
Sentiment analysis solutions apply consistent criteria to generate more accurate insights. For example, a machine learning model can be trained to recognise that there are two aspects with two different sentiments. It would average the overall sentiment as neutral, but also keep track of the details.
Sentiment analysis helps businesses make sense of huge quantities of unstructured data. When you work with text, even 50 examples already can feel like Big Data. Especially, when you deal with people’s opinions in product reviews or on social media.
Take the example of a company who has recently launched a new product. Rather than trawling through hundreds of reviews the company can feed the data into a feedback management solution. Its sentiment analysis model will classify incoming feedback according to sentiment. The company can understand what customers think of their new product faster and act accordingly. They can uncover features that customers like as well as areas for improvement.
This type of analysis also gives companies an idea of how many customers feel a certain way about their product. The number of people and the overall polarity of the sentiment about, let’s say “online documentation”, can inform a company’s priorities. For example, they could focus on creating better documentation to avoid customer churn and stay competitive.
Sentiment analysis algorithms can analyze hundreds of megabytes of text in minutes. Instead of manually analyzing data in spreadsheets, you can now spend your time on more valuable activities. For example, you can validate the insight: Is this something worth acting on? You can add business context too. If there is an issue, is it seasonal? Have we seen this in other parts of the business? Ultimately, sentiment analysis just provides a signal. But if you get this signal fast and with low effort, you will have time to create the right strategy.
Sentiment analysis algorithms and approaches are continually getting better. They are improved by feeding better quality and more varied training data. Researchers also invent new algorithms that can use this data more effectively. At Thematic, we monitor your results and assess errors. If required, we add more specific training data in areas that need improvement. As a result, sentiment analysis is becoming more accurate and delivers more specific insights.
Sentiment analysis is automated using Machine Learning. This means that businesses can get insights in real-time. This can be very helpful when identifying issues that need to be addressed right away. For example, a negative story trending on social media can be picked up in real-time and dealt with quickly. If one customer complains about an account issue, others might have the same problem. By instantly alerting the right teams to fix this issue, companies can prevent bad experiences from happening.
Sentiment analysis is useful for making sense of qualitative data that companies continuously gather through various channels. Let’s dig into some of the most common business applications.
Understanding how your customers feel about your brand or your products is essential. This information can help you improve the customer experience or identify and fix problems with your products or services. To do this, as a business, you need to collect data from customers about their experiences with and expectations for your products or services. This feedback is known as Voice of the Customer (VoC).
Net Promoter Score (NPS) surveys are a common way to assess how customers feel. Customers are usually asked, “How likely are you to recommend us to a friend?” The feedback is usually expressed as a number on a scale of 1 to 10. Customers who respond with a score of 10 are known as “promoters”. They’re the most likely to recommend the business to a friend or family member. High NPS means better customer retention. More promoters also means better word-of-mouth advertising. This means that you need to spend less on paid customer acquisition.
A drawback of NPS surveys is they don’t give you much information about why your customers really feel a certain way. Open-ended questions supplement the NPS rating questions. They capture why customers are likely or unlikely to recommend products and services. Sentiment analysis turns this text into the drivers of NPS.
NPS is just one of the VoC survey types. The same idea applies to any metric that you might care about: Customer Effort Score, Customer Satisfaction etc. It really doesn’t matter that much what metric is used. What’s driving the ups and downs of the metric is more important.
A great VOC program includes listening to customer feedback across all channels. You can imagine how it can quickly explode to hundreds and thousands of pieces of feedback even for a mid-size B2B company. Sentiment analysis is critical to make sense of this data.
Finally, companies can also quickly identify customers reporting strongly negative experiences and rectify urgent issues. Tracking your customers’ sentiment over time can help you identify and address emerging issues before they become bigger problems.
A great customer service experience can make or break a company. Customers want to know that their query will be dealt with quickly, efficiently, and professionally. Sentiment analysis can help companies streamline and enhance their customer service experience.
Sentiment analysis and text analysis can both be applied to customer support conversations. Machine Learning algorithms can automatically rank conversations by urgency and topic. For example, let’s say you have a community where people report technical issues. A sentiment analysis algorithm can find those posts where people are particularly frustrated. These queries can be prioritized for an in-house specialist. Regular questions can be answered by other community members.
As you can see, sentiment analysis can reduce processing times and increase efficiency by directing queries to the right people. Ultimately, customers get a better support experience and you can reduce churn rates.
Sentiment analysis can identify how your customers feel about the features and benefits of your products. This can help uncover areas for improvement that you may not have been aware of.
For example, you could mine online product reviews for feedback on a specific product category across all competitors in this market. You can then apply sentiment analysis to reveal topics that your customers feel negatively about. This could reveal opportunities or common issues.
For example, when we analyzed sentiment of US banking app reviews we found that the most important feature was mobile check deposit. Interestingly, most apps had issues with this feature. Companies that have the least complaints for this feature could use such an insight in their marketing messaging.
Product managers can iterate on improving the feature. They can then use sentiment analysis to monitor if customers are seeing improvements in functionality and reliability of the check deposit.
How customers feel about a brand can impact sales, churn rates, and how likely they are to recommend this brand to others. In 2004 the “Super Size” documentary was released documenting a 30-day period when filmmaker Morgan Spurlock only ate McDonald’s food. The ensuing media storm combined with other negative publicity caused the company’s profits in the UK to fall to the lowest levels in 30 years. The company responded by launching a PR campaign to improve their public image.
Sentiment analysis can help brands monitor how their customers feel about them. They can analyze communities, forums and social media platforms to keep an eye on their brand reputation. Or they can conduct surveys to understand what issues their customers feel strongly about.
Companies also track their brand, product names and competitor mentions to build up an understanding of brand image over time. This helps companies assess how a PR campaign or a new product launch have impacted overall brand sentiment.
Social media is a powerful way to reach new customers and engage with existing ones. Good customer reviews and posts on social media encourage other customers to buy from your company. But the reverse is also true. Negative social media posts or reviews can be very costly to your business.
Research by Convergys Corp. showed that a negative review on YouTube, Twitter or Facebook can cost a company about 30 customers. Negative social media posts about a company can also cause big financial losses. One memorable example is Elon Musk’s 2020 tweet which claimed the Tesla stock price was too high.
The viral tweet wiped $14 billion off Tesla’s valuation in a matter of hours. Sentiment analysis can help identify these types of issues in real-time before they escalate. Businesses can then respond quickly to mitigate any damage to their brand reputation and limit financial cost.
Sentiment analysis can help companies identify emerging trends, analyze competitors, and probe new markets. Companies may want to analyze reviews on competitors’ products or services. Applying sentiment analysis to this data can identify what customers like or dislike about their competitors’ products. These insights might reveal how to gain a competitive edge. For example, sentiment analysis could reveal that competitors’ customers are unhappy about the poor battery life of their laptop. The company could then highlight their superior battery life in their marketing messaging.
Sentiment analysis could also be applied to market reports and business journals to pinpoint new opportunities. For example, analyzing industry data on the real estate market could reveal a particular area is increasingly being mentioned in a positive light. This information might suggest that industry insiders see this area as a good investment opportunity. These insights could then be used to gain an early advantage by investing ahead of the rest of the market.
Atom bank is a newcomer to the banking scene that set out to disrupt the industry. They take customer feedback seriously. These insights are used to continuously improve their digital customer experiences.
Atom bank’s VoC programme includes a diverse range of feedback channels. They ran regular surveys, focus groups and engaged in online communities. This gave them A LOT of unstructured and structured data.
Working with Thematic, Atom bank transformed their banking experience. As you can see above, combining thematic and sentiment analysis identified what mattered most to their customers. Some themes such as “authentication” were associated with negative sentiment in Atom bank customer feedback. Other themes like “ease of use” were associated with positive sentiment.
Sentiment analysis also helped to identify specific issues like “face recognition not working”. Atom bank then used these insights to rectify these issues.
With all these customer sentiment insights, the team could prioritize the app features they knew would have the most impact. These improvements made Atom bank the highest rated bank according to Trustpilot. They also now have an App Store Rating of 4.7/5. And contact centre failure demand reduced by 30%!
Sentiment analysis uses machine learning and natural language processing (NLP) to identify whether a text is negative, positive, or neutral. The two main approaches are rule-based and automated sentiment analysis.
This is the traditional way to do sentiment analysis based on a set of manually-created rules. This approach includes NLP techniques like lexicons (lists of words), stemming, tokenization and parsing.
Rule-based sentiment analysis works like this:
“Lexicons” or lists of positive and negative words are created. These are words that are used to describe sentiment. For example, positive lexicons might include “fast”, “affordable”, and “user-friendly“. Negative lexicons could include “slow”, “pricey”, and “complicated”.
Before text can be analyzed it needs to be prepared. Several processes are used to format the text in a way that a machine can understand. Tokenization breaks up text into small chunks called tokens. Sentence tokenization splits up text into sentences. Word tokenization separates words in a sentence. For example, “the best customer service” would be split into “the”, “best”, and “customer service”. Lemmatization can be used to transforms words back to their root form. A lemma is the root form of a word. For example, the root form of “is, are, am, were, and been” is “be”. We also want to exclude things which are known but are not useful for sentiment analysis. So another important process is stopword removal which takes out common words like “for, at, a, to”. These words have little or no semantic value in the sentence. Applying these processes makes it easier for computers to understand the text.
A computer counts the number of positive or negative words in a particular text. A special rule can make sure that negated words, e.g. “not easy”, are counted as opposites.
The final step is to calculate the overall sentiment score for the text. As mentioned previously, this could be based on a scale of -100 to 100. In this case a score of 100 would be the highest score possible for positive sentiment. A score of 0 would indicate neutral sentiment. The score can also be expressed as a percentage, ranging from 0% as negative and 100% as positive.
Rule-based approaches are limited because they don’t consider the sentence as whole. The complexity of human language means that it’s easy to miss complex negation and metaphors. Rule-based systems also tend to require regular updates to optimize their performance.
Automated sentiment analysis relies on machine learning (ML) techniques. In this case a ML algorithm is trained to classify sentiment based on both the words and their order. The success of this approach depends on the quality of the training data set and the algorithm.
There are also hybrid sentiment algorithms which combine both ML and rule-based approaches. They can offer greater accuracy, although they are much more complex to build.
Before the model can classify text, the text needs to be prepared so it can be read by a computer. Tokenization, lemmatization and stopword removal can be part of this process, similarly to rule-based approaches.In addition, text is transformed into numbers using a process called vectorization. These numeric representations are known as “features”. A common way to do this is to use the bag of words or bag-of-ngrams methods. These vectorize text according to the number of times words appear.
Recently deep learning has introduced new ways of performing text vectorization. One example is the word2vec algorithm that uses a neural network model. The neural network can be taught to learn word associations from large quantities of text. Word2vec represents each distinct word as a vector, or a list of numbers. The advantage of this approach is that words with similar meanings are given similar numeric representations. This can help to improve the accuracy of sentiment analysis.
In the next stage, the algorithm is fed a sentiment-labelled training set. The model then learns to associate input data with the most appropriate corresponding label. For example, this input data would include pairs of features (or numeric representations of text) and their corresponding positive, negative or neutral label. The training data can be either created manually or generated from reviews themselves.
The final stage is where ML sentiment analysis has the greatest advantage over rule-based approaches. New text is fed into the model. The model then predicts labels (also called classes or tags) for this unseen data using the model learned from the training data. The data can thus be labelled as positive, negative or neutral in sentiment. This eliminates the need for a pre-defined lexicon used in rule-based sentiment analysis.
Classification algorithms are used to predict the sentiment of a particular text. As detailed in the vgsteps above, they are trained using pre-labelled training data. Classification models commonly use Naive Bayes, Logistic Regression, Support Vector Machines, Linear Regression, and Deep Learning. Let’s explore these algorithms in a bit more detail.
Naive Bayes: this type of classification is based on Bayes’ Theorem. These are probabilistic algorithms meaning they calculate the probability of a label for a particular text. The text is then labelled with the highest probability label. “Naive” refers to the fundamental assumption that each feature is independent. Individual words make an independent and equal contribution to the overall outcome. This assumption can help this algorithm work well even where there is limited or mislabelled data.
Logistic Regression: a classification algorithm that predicts a binary outcome based on independent variables. It uses the sigmoid function which outputs a probability between 0 and 1. Words and phrases can be either classified as positive or negative. For example, “super slow processing speed” would be classified as 0 or negative.
Linear Regression: algorithm that predicts polarity (Y output) based on words and phrases (X input). The objective is to learn a linear model or line which can be used to predict sentiment (Y). Accuracy of the model can be improved by reducing the error.
Support Vector Machines: a model that plots labelled data as points in a multi-dimensional space. The hyperplane or decision boundary is a line which divides the data points. In the example below, anything to the left of the hyperplane would be classified as negative. And everything to the right would be classified as positive. The best hyperplane is one where the distance to the nearest data point of each tag is the largest. Support vectors are those data points which are closer to the hyperplane. They influence its position and orientation. These are the points which help to build the support vector machine.
Deep Learning: here, an artificial neural network performs multiple layers of processing. Deep learning is a diverse set of algorithms that imitate human brain learning through associations and abstractions. Deep learning has significant advantages over traditional classification algorithms. These neural networks can understand context, and even the mood of the writer.
It’s worth exploring deep learning in more detail since this approach results in the most accurate sentiment analysis. Up until recently the field was dominated by traditional ML techniques, which require manual work to define classification features. They also often fail to consider the impact of word order. Deep learning and artificial neural networks have transformed NLP.
Deep learning algorithms were inspired by the structure and function of the human brain. This approach led to an increase in the accuracy and efficiency of sentiment analysis. In deep learning the neural network can learn to correct itself when it makes an error. With traditional machine learning errors need to be fixed via human intervention.
One important Deep Learning approach is the Long Short-Term Memory or LSTM. This approach reads text sequentially and stores information relevant to the task.
The LSTM consists of three parts which are known as “gates”:
Forget Gate: This first part decides whether previous data is to be remembered. If it is irrelevant to the task, it can be forgotten.
Input Gate: In the second part the cell tries to learn new information from the new data.
Output Gate: The final part is where the cell passes updated information to the next timestamp.
For sentiment analysis it’s useful that there are cells within the LSTM which control what data is remembered or forgotten. Negation is crucial in accurate sentiment analysis. For example, it’s obvious to any human that there’s a big difference between “great” and “not great”. An LSTM is capable of learning that this distinction is important and can predict which words should be negated. The LSTM can also infer grammar rules by reading large amounts of text.
LSTMs have their limitations especially when it comes to long sentences. The model can often forget the content of distant words. And the sentence has to be processed word by word.
An alternative solution is to use a transformer. This model differentially weights the significance of each part of the data. Unlike a LTSM, the transformer does not need to process the beginning of the sentence before the end. Instead it identifies the context that confers meaning to each word. This is known as an attention mechanism. Transformers have now largely replaced LTSMs as they’re better at analysing longer sentences.
Pre-trained models allow you to get started with sentiment analysis right away. It’s a good solution for companies who do not have the resources to obtain large datasets or train a complex model.
Sentiment analysis uses machine learning and natural language processing (NLP) to identify whether a text is negative, positive, or neutral. The two main approaches are rule-based and automated sentiment analysis.
Texts can be objective or subjective.
Consider the following sentences as an example:
The first sentence is clearly subjective and most people would say that the sentiment is positive. The second sentence is objective and would be classified as neutral. In this “good” is considered more subjective than “small”.
The challenge here is that machines often struggle with subjectivity. Let’s take the example of a product review which says “the software works great, but no way that justifies the massive price-tag”. In this case the first half of the sentence is positive. But it’s negated by the second half which says it’s too expensive. The overall sentiment of the sentence is negative.
Large training datasets that include lots of examples of subjectivity can help algorithms to classify sentiment correctly. Deep learning can also be more accurate in this case since it’s better at taking context and tone into account.
Context is crucial when it comes to understanding sentiment. Opinion words can change their polarity depending on the context. Machines need to learn about context in order to correctly classify a text.
For example, the question “what did you like about our product” could produce the following answers:
The first answer would be classified as positive. The second answer is also positive, but on its own it is ambiguous. If we changed the question to “what did you not like”, the polarity would be completely reversed. Sometimes, it’s not the question but the rating that provides the context.
The solution to this is to preprocess or postprocess the data to capture the necessary context. This can be a complex and lengthy process.
Humour and sarcasm can present big challenges for machine learning techniques! Take the real life example of a complaint letter sent to LIAT Caribbean Airlines by passenger Arthur Hicks:
With irony and sarcasm people use positive words to describe negative experiences. It can be tough for machines to understand the sentiment here without knowledge of what people expect from airlines. In the example above words like ‘considerate” and “magnificent” would be classified as positive in sentiment. But for a human it’s obvious that the overall sentiment is negative.
Luckily, in a business context only a very small percentage of reviews use sarcasm.
Comparison is another potential stumbling block to correct sentiment classification. Consider these example online reviews:
In the first case it’s obvious sentiment is positive. The second one is trickier since they rely on comparisons. Without knowing what the product is being compared to, it’s hard to know if these are positive, negative or neutral. In the second sentence it depends on the “alternatives”. If the person considers the other products they’ve used to be very poor, this sentence could be less positive than it seems at face value.
If you are company X and your competitor is company Y, it is impossible to have one sentiment model that captures positive sentiment about Y as negative sentiment about X. Let’s say you get these comments:
I love the service that I get from company X
I love the service that I get from company Y
A general model can only say both are positive. If you want to say that a comment speaking highly of your competitor is negative, then you need to train a custom model.
Emojis can require extensive preprocessing especially when using data sources like social media platforms. There are two key types of emojis, Western emojis and Eastern emojis. Western emojis use only a couple of characters, such as :). Eastern emojis use more characters in a vertical combination, such as ¯\_(ツ)_/¯ which means something like “smiley sideways shrug” in Japan.
Machine Learning algorithms struggle with idioms and phrases. An example is “not my cup of tea”. This would potentially confuse the algorithm. If a reviewer uses an idiom in product feedback it could be ignored or incorrectly classified by the algorithm. The solution is to include idioms in the training data so the algorithm is familiar with them.
For accurate sentiment analysis defining the neutral label appropriately is important. The criteria need to be consistent to generate good quality and reliable analysis. Examples of texts that should be classified as neutral include objective statements like the example we looked at above: “This laptop is black”. There are no obvious sentiments expressed in this sentence.
Irrelevant data can be classified as neutral. Another approach is to filter out any irrelevant details in the preprocessing stage.
Use of the word “wish” may indicate neutral sentiment. Consider the example, “I wish I had discovered this sooner.” However, you’ll need to be careful with this one as it can also be used to express a deficiency or problem. For example, a customer might say, “I wish the platform would update faster!” This word can express a variety of sentiments.
Negation can also create problems for sentiment analysis models. For example, if a product reviewer writes “I can’t not buy another Apple Mac'' they are stating a positive intention. Machines need to be trained to recognize that two negatives in a sentence cancel out.
As mentioned earlier, a Long Short-Term Memory model is one option for dealing with negation efficiently and accurately. This is because there are cells within the LSTM which control what data is remembered or forgotten. A LSTM is capable of learning to predict which words should be negated. The LSTM can “learn” these types of grammar rules by reading large amounts of text.
Negation can also be solved by using a pre-trained transformer model and by carefully curating your training data. Pre-trained transformers have within them a representation of grammar that was obtained during pre-training. They are also well suited to parallelization, making them efficient for training using large volumes of data. Curating your data is done by ensuring that you have a sufficient number of well-varied, accurately labelled training examples of negation in your training dataset.
Video and audio are a very different type of data to text. Audio on its own or as part of videos will need to be transcribed before the text can be analyzed using Speech-to-text algorithm. Sentiment analysis can then analyze transcribed text similarly to any other text. There are also approaches that determine sentiment from the voice intonation itself, detecting angry voices or sounds people make when they are frustrated. These techniques can also be applied to podcasts and other audio recordings.
As we mentioned above, even humans struggle to identify sentiment correctly. This can be measured using an inter-annotator agreement, also called consistency, to assess how well two or more human annotators make the same annotation decision. Since machines learn from training data, these potential errors can impact on the performance of a ML model for sentiment analysis.
Based on a recent test, Thematic’s sentiment analysis correctly predicts sentiment in text data 96% of the time. But we also talked extensively about the meaning of accuracy and how one should take any reports of accuracy with a grain of salt.
That said, when it comes to aspect based sentiment analysis (ABSA), as defined earlier, we did run a study where we compared aspects discovered by 4 people vs. aspects discovered by Thematic. We learned that on average, Thematic agrees with people more than they agree with each other!
Getting started with sentiment analysis can be intimidating. Luckily there are many online resources to help you as well as automated SaaS sentiment analysis solutions. Or you might choose to build your own solution using open source tools.
Should you build your own or invest in existing software? The answer probably depends on how much time you have and your budget. Usually, building inhouse is more expensive. Let’s dig into the details of building your own solution or buying an existing SaaS product.
Building your own sentiment analysis solution can be a lengthy and complex process. The steps required to build this type of tool are:
The first step is to understand which machine learning options are best for your business. You’ll need to consider the programming language to use as well.
You can develop the algorithms yourself or, most likely, use an off-the shelf model.
The model is fed a sentiment-labelled training set. The model then learns to associate input data with the most appropriate corresponding labels. This can be time-consuming as the training data needs to be curated, labelled or generated. Integrate: Build an API or manually integrate the model with your existing tools. You may also need to construct a user-friendly interface if your tool will be used by non-technical colleagues.
Non-technical teams in particular may require detailed onboarding training on how to use the tool. You may need to create internal training manuals. Launch: The final phase is to start using your tool within your business. Regular monitoring and tweaking may be required to optimize performance.
The tool can be customized to meet your exact business requirements.
Building your own sentiment analysis solution takes considerable time. The minimum time required to build a basic sentiment analysis solution is around 4-6 months. You may need to hire or reassign a team of data engineers and programmers. Creating custom software may take longer than you had planned. Deadlines can easily be missed if the team runs into unexpected problems. This can cause costs to increase significantly. Once the tool is built it will need to be updated and monitored. It’s a custom-built solution so only the tech team that created it will be familiar with how it all works.
Python is a popular programming language to use for sentiment analysis. An advantage of Python is that there are many open source libraries freely available to use. These make it easier to build your own sentiment analysis solution.
Here are some resources that can help you use Python for sentiment analysis:
NLTK or Natural Language Toolkit is one of the main NLP libraries for Python. It includes useful features like tokenizing, stemming and part-of-speech tagging. NLTK also has a pretrained sentiment analyzer called VADER (Valence Aware Dictionary and sEntiment Reasoner). VADER works better for shorter sentences like social media posts. It can be less accurate when rating longer and more complex sentences.
spaCy is another NLP library for Python that allows you to build your own sentiment analysis classifier. Like NLTK it offers part-of-speech tagging and named entity recognition.
PyTorch is a machine learning library primarily developed by Facebook’s AI Research lab. It is popular with developers thanks to its simplicity and easy integrations.
You might also find these tutorials helpful:
NLTK has developed a comprehensive guide to programming for language processing. It covers writing Python programs, working with corpora, categorizing text, and analyzing linguistic structure.
This beginner’s guide from Towards Data Science covers using Python for sentiment analysis.
Java is another popular language for sentiment analysis. Here’s a list of useful toolkits for Java:
OpenNLP is an Apache toolkit which uses machine learning to process natural language text. It supports tokenization, part-of-speech tagging, named entity extraction, parsing, and much more.
The Stanford CoreNLP NLP toolkit also has a wide range of features including sentence detection, tokenization, stemming, and sentiment detection.
Another open source option for text mining and data preparation is Weka. This collection of machine learning algorithms features classification, regression, clustering and visualization tools.
Take a look at this tutorials to learn more about using Java for sentiment analysis:
This Red Hat tutorial looks at performing sentiment analysis of Twitter posts using Stanford CoreNLP.
There are a variety of pre-built sentiment analysis solutions like Thematic which can save you time, money, and mental energy.
Let’s consider the pros and cons of using a SaaS solution for sentiment analysis:
SaaS products like Thematic allow you to get started with sentiment analysis straight away. You can instantly benefit from sentiment analysis models pre-trained on customer feedback.
No coding is needed. This makes SaaS solutions ideal for businesses that don’t have in-house software developers or data scientists.
Costs are a lot lower than building a custom-made sentiment analysis solution from scratch.
One-click integrations into feedback collection tools and APIs enable seamless and secure data transfer.
Access to comprehensive customer support to help you get the most out of the tool.
There are many sentiment analysis solutions on the market. It can be hard to choose the right one for your business.
For many businesses the most efficient option is to purchase a SaaS solution that has sentiment analysis built in. Thematic is a great option that makes it easy to perform sentiment analysis on your customer feedback or other types of text.
Thematic uses sentiment analysis algorithms that are trained on large volumes of data using machine learning. A unique feature of Thematic is that it combines sentiment with themes discovered during the thematic analysis process.
Before we dig into the benefits of combining sentiment analysis and thematic analysis, let’s quickly review these two types of analysis.
Thematic analysis is the process of discovering repeating themes in text. A theme captures what this text is about regardless of which words and phrases express it. For example, one person could say “the food was yummy”, another could say “the dishes were delicious”. In both cases, it’s the same theme. We could call it “tasty food”.
AI researchers came up with Natural Language Understanding algorithms to automate this task. Thematic software is powered by these algorithms. You can learn more about how it works in our blog post.
We talked earlier about Aspect Based Sentiment Analysis, ABSA. Themes capture either the aspect itself, or the aspect and the sentiment of that aspect. In addition, for every theme mentioned in text, Thematic finds the relevant sentiment.
Let’s walk through how you can use sentiment analysis and thematic analysis in Thematic to get more out of your textual data.
The first step is to upload your unstructured data to a feedback analytics tool like Thematic. This could include online survey feedback, chat conversations, or social media mentions. Thematic has a wide range of one-click integrations that make it really easy to connect all your channels. These include Qualtrics, Trustpilot, Amazon, Facebook, Intercom, Twitter, Tripadvisor, and many more. Thematic then automatically cleans and prepares your data so it’s ready to be analyzed.
Thematic analysis can then be applied to discover themes in your unstructured data. Thematic’s AI groups themes into a 2-level taxonomy. For a given text there will be core themes and related sub-themes. For example, a core theme could be “staff behavior”. A sub-theme could be “friendly crew”. This helps you easily identify what your customers are talking about, for example, in their reviews or survey feedback.
Sentiment analysis builds on thematic analysis to help you understand the emotion behind a theme. Sentiment analysis scores each piece of text or theme and assigns positive, neutral or negative sentiment.
In the example above the theme “print boarding passes” has been selected within the Thematic dashboard. Here you can get an overview of the sentiment associated with this theme across your textual data. Overall this theme has negative sentiment with 61.2% of theme appearances classified as negative. You can also see that this theme appears in 0.4% of customer reviews.
Another option is to filter your themes by sentiment. This allows you to quickly identify the areas of your business where customers are not satisfied. You can then use these insights to drive your business strategy and make improvements.
Combining these two types of analysis can be very powerful. It allows you to understand how your customers feel about particular aspects of your products, services, or your company.
Combining Thematic and Sentiment analysis can also help you understand metrics like NPS or customer churn.
This example from the Thematic dashboard tracks customer sentiment by theme over time. You can see that the biggest negative contributor over the quarter was “bad update”. This makes it really easy for stakeholders to understand at a glance what is influencing key business metrics.
With Thematic you also have the option to use our Customer Goodwill metric. This score summarizes customer sentiment across all your uploaded data. It allows you to get an overall measure of how your customers are feeling about your company at any given time.
In the example below you can see the overall sentiment across several different channels. These channels all contribute to the Customer Goodwill score of 70.
Thematic’s platform also allows you to go in and make manual tweaks to the analysis. Combining the power of AI and a human analyst helps ensure greater accuracy and relevance.
For example, you may want to scan through the themes and delete any which are not useful. You also have the option to merge themes together, create new themes, and switch between themes and sub-themes.
The final step in the process is continual real-time monitoring. This can help you stay on top of emerging trends and rapidly identify any PR crises or product issues before they escalate.
In the example above you can see sentiment over time for the theme “chat in landscape mode”. The visualization clearly shows that more customers have been mentioning this theme in a negative sentiment over time. Looking at the customer feedback on the right indicates that this is an emerging issue related to a recent update. Using this information the business can move quickly to rectify the problem and limit possible customer churn.
Sentiment analysis uses machine learning and natural language processing (NLP) to identify whether a text is negative, positive, or neutral. The two main approaches are rule-based and automated sentiment analysis.
For those who want a really detailed understanding of sentiment analysis there are some great books out there. One of the classics is “Sentiment Analysis and Opinion Mining” by Bing Liu. Liu is considered a thought-leader in machine learning. His book is great at explaining sentiment analysis in a technical yet accessible way.
If you’d like to know more about deep learning for sentiment analysis, a great option is “Deep Learning-Based Approaches for Sentiment Analysis”. It was published in 2020 and includes insights into the latest trends and advances in deep learning for sentiment analysis.
Those especially interested in social media might want to look at “Sentiment Analysis in Social Networks”. This specialist book is authored by Liu along with several other ML experts. It looks at natural language processing, big data, and statistical methodologies.
The field of sentiment analysis is always evolving and there’s a constant flow of new research papers. Here’s a selection of recent papers for those who want to dig deeper into specific subtopics:
There are plenty of online resources to help you learn how to do sentiment analysis using NLP. Here’s a selection to help you get started:
To get going with sentiment analysis you may need access to suitable datasets if you don’t already have your own data. Here’s a selection of freely-available datasets which you can use to experiment with sentiment analysis:
Those looking at a rule-based approach will need sentiment analysis lexicons or lists of words that have been pre-labelled with sentiment. Here are some useful options:
We hope this guide has given you a good overview of sentiment analysis and how you can use it in your business. Sentiment analysis can be applied to everything from brand monitoring to market research and HR. It’s helping companies to glean deeper insights, become more competitive, and better understand their customers.
Sentiment analysis is also a fast-moving field that’s constantly evolving and developing. That’s why it’s important to stay on top of the latest trends. Another option is to work with a platform like Thematic that’s continually being upgraded and improved. For more information about how Thematic works you can request a personalized guided trial right here.
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