Sentiment Analysis: A Comprehensive Guide

The power of Natural Language Processing (NLP) enables organizations to implement sentiment analysis to gather valuable information about their customer behaviors and patterns.   

Satisfied customers are a constant source of happiness for businesses. Usually, every business sets one goal in mind: how to perpetually enhance customer satisfaction, retain current customers, and drive more ones.

That’s why customer feedback is of paramount importance in helping a business realize its customer sentiment about specific products or services. This blog will explain sentiment analysis in detail and how you can apply sentiment analysis to analyze customer feedback. 

What Is Sentiment Analysis?

Sentiment analysis is an NLP and machine learning technique deciding whether customer feedback is positive or negative.   

Sentiment analysis in data science is conducted on textual data such as online reviews or social media comments, which often illustrate how customers are feeling about a brand, specific topic, or advertisement.   

Why Sentiment Analysis Matters?

Sentiment analysis helps you understand how customers feel about your brand and products on a large scale. All data including customer feedback, surveys, social media posts and mentions is used in sentiment analytics to provide valuable business insights, which eventually help business leaders to make informed decisions. Four benefits of sentiment analysis: 

Eliminate Human Bias and Make the Analysis More Trustworthy

When humans want to convey meaning with speech or text, we use language, tone, and context. That means sentiment is highly subjective. We may interpret things differently based on our experience and probably unconscious bias.  

For example:  “The hair dryer works just as the description, but it’s not the best price I get.”  

This review contains both positive and negative sentiments. The feedback for the product price conveys negative sentiment, whereas the product is a good fit for the need of customers. This is when human bias can affect the overall view of the feedback. An analyst may conclude it as positive sentiment because he focuses on the product’s function. But another analyst may terminate it as negative sentiment because he considers the price element more critical.  

The sentiment analysis model can recognize both aspects of the feedback and regard the feedback as neutral sentiment.   

Process Data on a Large Scale

As stated above, sentiment doesn’t only include text textual customer reviews, but also data from survey and social media listening (posts, shares, mentions). The data is unstructured and huge, so it’s impossible for people to process it manually. Sentiment analytics helps companies analyze and extract information in time. With valuable data-driven facts, they can act faster in responding to their customers’ feedback. 

Everything is Automated

Instead of wasting time for manual analysis in spreadsheets, you can employ sentiment analysis algorithms to analyze megabytes of text in seconds. You can spare your time for other meaningful activities such as validating the insight.  

• Should I act on it? 
• Is this problem seasonal?  

After all the outcome of sentiment analysis is signal, it’s your job to act according to what you just found. 

Real-time Analysis

With Machine Learning, sentiment analysis is automated and able to provide real-time insights. With fast signal and low effort, you can quickly identify issues and act on them right away. Furthermore, the algorithms and approaches are improved continuously thanks to constant feeding data and training.  

Sentiment Analysis Methods and Types

Sentiment analysis’s main focus is on the text’s polarity which contains positive, negative, and neutral. Companies can also apply emotion analytics to identify customer feelings through reviews, conversations and surveys. Here are 5 types of sentiment analysis: 

Document-level Sentiment Analysis

Document-level sentiment analysis categorizes the sentiment or motion depending on the information given. In basic text analytics, semantics in a document can be concluded from three areas: word representation, sentence structure and composition, and document composition.

It is easy as long as there is only one sentiment in the complete text. Nonetheless, this method is not very useful if the word representation and sentence composition are complex. In such situations, the variables of the comment can be lost, and the results will be incorrect.   

Let’s take a look at restaurant reviews:  

‘Went to Tera Nara and loved their food’: This feedback can be analyzed and regarded as positive because it is a brief, composite sentence, with one clear sentiment.  

‘Went to Tera Nara and loved their food, but the drinks were awful’: This is the case when the review is longer, covering other topics (food and drinks).  

Topic-based Sentiment Analysis

Topic-based sentiment analysis looks for the sentiment related to a particular topic. This approach discovers and extracts topics in the data through keywords and aggregate scoring. It also takes the mood into consideration reflected on the topic.   

A machine learning model can be customized for every topic, company, or industry requirement. For instance, topics related to hospitality can be reservations, food, or service, while in healthcare, it can be the ER, prescription dosage, patient wait-time, etc.,  

Aspect-based Sentiment Analysis

Aspect-based sentiment analysis discovers the primary aspects of features of an entity and offers an estimate of the average sentiment for each aspect. For instance, an entity could be a luxury bag whose features could be its design, colors, etc. Thus, aspect-based sentiment analysis can make your review analysis more detailed and accurate. 

Graded Sentiment Analysis

If your company wants to focus on polarity, you should expand the categories of polarity to a greater variety of levels: 

Normally we have three levels:  

• Negative 
• Neutral 
• Positive 

What we recommend are: 

• Very negative 
• Negative 
• Neutral 
• Positive 
• Very positive 

Customers’ reviews are normally rated from 1 star to 5 stars, so the recommended polarity level corresponds well with the ratings. This is called graded sentiment analysis. 

Emotion Detection Analysis

Emotion analysis using deep learning allows you to identify customer emotions such as happy, angry, frustrated, sad…. 

Complex machine learning algorithms or lexicons (list of emotion words) are used to detect the emotion underlined in customer feedback.  

If you want to use lexicons, you should consider that lexicons have weakness because there are words that look the same but convey different meanings. 

For example,  

• This product is so bad -> emotion analytics: anger 
• This shirt is badass -> emotion analytics: happy 

Multilingual Sentiment Analysis

Multilingual sentiment analysis is difficult because you need to be well-prepared with preprocessing and resource. You can find many resources online such as sentiment lexicons or you may need to create the translated corpora algorithms.  

How to Employ Sentiment Analysis for Analyzing Customer Feedback?

Sentiment Analysis Based on the Quantity of Data

Thanks to the support of Machine Learning and Natural Language Processing algorithms, a business can automatically identify the sentiment of customer conversation.   

A lot of algorithms have been revised and improved to promote building models to analyze sentiment according to the amount of data required for the analysis and precision of the model. Let’s see them in detail.   

Rule-based Approach

A rule-based approach or manual approach consists of rules created by humans to define polarity, subjectivity, the viewpoint of approval or refusal.   

Here’s an example of how rule-based systems work:    

• Compile two sets of positive words (good, beautiful, incredible, pleased) and negative polarized words (worst, awful, rude, terrible) 
• Calculate the number of positive and negative words that appear in the corpus  
• After analysis, if the number of negative responses is higher than the positive, then the result is negative sentiment. Meanwhile, if the number of polarities is equal, the result would be neutral.  

However, the work is completely manual and does not apply any pattern to organize and combine the words for a sequence. Hence, it cannot manipulate huge amounts of data when you create new rules to support enormous vocabulary. That results in a complicated system and possibly past result loss. Thus, the method will not work with vast or complex data.   

Automatic System

An automatic system is contrary to a rule-based system. It employs machine learning, where a classifier is given a piece of text and sends back an outcome as positive, negative, or neutral.

Here’s the implementation of the machine learning classifier and how it is implemented:  

The model train learns from input test data. At the same time, during the training and prediction process, the feature extractor converts unseen text inputs into vectors and then brings them to the model to predict positive, negative, and neutral tags. Several of the classification algorithms utilized for such models are Logistic Regression, Support Vector Machine, Naïve Bayes, and so on.   

Hybrid System

A hybrid system is a mix of the Rule-based system and the Automatic system. It operates as the most effective and precise system since it balances manual and automatic processes.   

Lastly, the aim of customer feedback is to allow a company to understand its customer behaviors towards a specific brand, topic, product, service, and advertisement.   

Hence, the company can now easily analyze meaningful information employing Natural Language Processing and Machine Learning. That allows them to advance customer satisfaction, products, and services and produce recommendations based on customers’ preferences.   

Sentiment Analysis Based on a Sentiment Score

According to the covered words and the associated sentiment in the customer responses, the sentiment analysis approach sets a sentiment score for them. Therefore, positive feedback gathers a higher sentiment score while negative one gets a lower score.   

With the sentiment score, data analysts can examine user feedback by one of the below methods:   

Define the average sentiment score

The average sentiment score indicates the overall user feedback. A high average score means positive feedback, which means positive sentiments equal a dominant share in the feedback. In the meantime, a low or negative score represents major negative feedback.    

Measure a sentiment histogram

A sentiment histogram offers a visual display of how your sentiment scores are allocated. A histogram indicates the point where most of the sentiments are distributed.   

Build a word cloud

A sentiment score can show either a positive or negative response, while a word cloud can help examine the actual words used to impart customer sentiment.   

Creating a word cloud can improve the understanding of feedback themes or topics being discussed in the feedback. For instance, words that typically express feedback consist of ‘reliable,’ ‘excellent,’ ‘interesting,’ and ‘good.’ 

Sentiment Analysis Use Cases

Voice of Customer (VoC)

VoC reflects the customer experience with your brands and products as well as their expectations. You can ask for their feedback through surveys such as Net Promoter Score (NPS) surveys (For example, “How likely are you to recommend us to a friend?” Customers give answers in scale of 1 to 10) and open-ended surveys (For example, questions like “What functions do you think needs improvement?”. Surveys are great but still limited because customers may not reveal their feelings and answer with caution or bias.  

That’s why sentiment analysis is critical for brands to “listen” to their customers actively on all platforms and channels including surveys, customer support conversations, social media mentions 

Customer Service Experience

Customer service is among the top concerns in companies’ operation because it decides the customer experience as a positive or negative one. Customers always want their request to be received and solved in a quick and professional manner.  

Sentiment analysis analyze customer support conversations and rank them by topics and urgency. It can run through the support center and detect forum posts from frustrated customers. Companies can urgently distribute specialists to answer those posts first, thus preventing negative customer experience.  

Product Experience

With sentiment analysis, you can do data mining on your product’s online reviews, then analyze to unveil topics that customers have negative feelings towards. This is your opportunity to fix the issue and improve the overall product’s experience.  

You can also use analysis to know whether your customers are seeking for function improvements and make your move.  

Brand Sentiment Analysis

Customer feedback has a major impact on the brand’s image. Sentiment analysis can help brands constantly check what their customers are talking/feeling about them through forums, social media platforms, and surveys.  

Brands can also keep a close watch on their competitors’ reputation with the online data that they can easily collect.  

Social Media Sentiment Analysis

Social media platforms are a great way to communicate with current customers and reach potential ones. While positive reviews, posts and mentions of your brand on social media help you gain an overall impression of your brand for new customers, negative reviews can destroy your brand’s image in the same way.  

That’s why sentiment analysis is necessary to monitor your brand reputation because it can process large amounts of unstructured data on social media and notify you of negative mentions. 

Market Research

Sentiment analysis detects new trends, analyzes competitors and investigates new markets. Companies can make use of their competitor’s online customer reviews to analyze and uncover the weaknesses. You will know what customers dislike or like about your competitor’s products, thus creating your own competitive edge.  

You can also use sentiment analysis in market reports and business journals to reveal any new opportunities possible. You can spot out information suggesting big potential and decide to invest in it.  

Sentiment Analysis Challenges

Sentiment analysis is improving and getting better, but we have to admit that it is one of the most difficult tasks in Natural language processing. Here are the break-down and examples of 7 challenges: 

Subjectivity

There are two text types: objective and subjective. Subjective texts include explicit sentiment whereas objective texts do not contain such explicit sentiment.  

The shirt is great -> subjective texts, positive sentiment  

The shirt is yellow -> objective texts, neutral sentiment 

Humans can easily point this out and classify the sentiment as above, but it is a struggle for machines. Machine algorithms can improve if trained with large datasets including many examples of subjectivity.  

Context

Texts can’t be taken out of context because they may mean something completely different. It’s the same with understanding sentiment because words can change polarity based on the context.  

Machines need to learn about context to classify text in a correct way, so let’s look at this example: 

“What do you like about the dress?”  

“nice color” -> positive sentiment 

“material” -> positive sentiment but it’s not clear. If the question is switched to “What do you not like about the dress?”  the polarity will be negative for this answer. 

The solution is to postprocess and preprocess data to get the context, which is complicated and takes time. 

Sarcasm

Irony or sarcasm is a big challenge for machine learning because it contains positive words to emphasize a negative experience. Machines can’t classify the sentiment without understanding it deeply as human. Fortunately, not a lot of customer feedback is sarcastic in a business context. 

Comparison

Let’s see some examples: 

This is second to none -> positive sentiment 

This is better than nothing -> could be neutral, negative sentiment 

Comparison is another challenge to correctly classify sentiment because there is no contextual clues here. 

Emojis

Emojis can be categorized into two types:  

• Western emojis: include 1 to 2 characters, for example ? ?  

• Japanese emojis: include many characters, for example ( ͡° ͜ʖ ͡° ) ⸂⸂⸜(രᴗര๑)⸝⸃⸃  

We use emojis a lot in social media posts nowadays, so we need to prepare the preprocessing carefully. Both Western and Japanese emojis need to be turned into tokens and added to whitelist during preprocessing of social media data. This will help to improve the sentiment analysis.  

Mention Competitors

Here is an example: 

• X: your company 
• Y: your competitor 

Customer reviews: “I like the service from company Y” 

A general machine model can only classify the review as positive. For humans, it is obvious that the sentiment is negative in this case. You need to train a custom model to recognize that a review complimenting your competitor is negative. 

Idioms

Idioms and phrase are big challenges for Machine Learning algorithms, for example: 

“This product is not my cup of tea” => negative sentiment 

This review confuses the algorithm, so texts with idioms are normally skipped or classified incorrectly. You need to provide training data with lots of idioms so that the algorithm can process them. 

Neutrality

To generate good quality analysis, the neutral sentiment should be classified correctly. For example, a text such as “This dress is black” clearly indicates neutral sentiment because there is no clear feeling expressed in that sentence.

However, data which is irrelevant can also be in the category of neutral sentiment. You need to prepare the preprocessing to remove irrelevant details to solve this neutrality challenge. 

Human Annotator Accuracy

Inter-annotator agreement is a measurement of how two or more humans can make the same annotation decision, or you can simply understand it as “Consistency” which is important to ensure good quality and consistent analysis.

Even humans can encounter difficulty when classifying sentiments. Machines learn from training data so these human errors can directly affect the performance of the Machine Learning model. 

Sentiment Analysis Tools

We have put together a list of 10 sentiment analysis tools free and paid. Let’s explore them together: 

Sentiment Analysis Resources and Tutorials

We have put together a list of useful books and tutorials for you. Let’s dive right in: 

• Book: “Sentiment Analysis and Opinion Mining”  
• Book: “Deep Learning-Based Approaches for Sentiment Analysis 
• Book: “Sentiment Analysis in Social Networks” 
• Research paper: “Sentiment Analysis and Subjectivity” by Bing Liu. 
• Research paper: “Sentiment Analysis of Twitter Data” by Apoorv Agarwal et al. 
• Research paper: “Machine learning based customer sentiment analysis for recommending shoppers, shops based on customers’ review” by Shanshan Yi & Xiaofang Liu. 
• Research paper: “Sentiment Analysis in English Texts” by Arwa A. Al Shamsi, Reem Bayari and Said Salloum. 
• Paper: “Sentiment Analysis for Social Media” by Carlos Iglesias and Antonio Moreno. 
• Tutorials and online courses: “Sentiment Analysis, Beginner to Expert”. by Udemy. 
• Tutorials and online courses: “Natural Language Processing (NLP) in Python” by Udemy. 

Wrapping Up!

By using sentiment analysis, you can measure how customers feel about various aspects of your organization without reading tons of customer comments.   

Leveraging sentiment analysis and automating this process, you will easily dive deep into different customer segments of your company and gain a better understanding of sentiment in these segments. 

If you have any question, feel free to contact with us!

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