A Definitive Guide to Sentiment Analysis

Customer is the driving force of any business. Knowing what they think of your product and service will help your organization go a long way. With Sentiment analysis tools, you can easily find out about your customers from feedback data. 

Sentiment analysis plays a big role in understanding your audience and customers. This method lets you gather crucial insights from unorganized bulk data with the help of applications. 

Let’s dive into opinion mining, its types, impotence, challenges, working methods, and real-life examples.  

What Is Sentiment Analysis?

Sentiment analysis means identifying the emotion or sentiment through text analysis and mining. It is also known as opinion mining. Companies can use this approach to categorize their opinions on their products and services. Besides sentiment determination, this analysis can gather the text’s polarity, subject, and opinion. 

Opinion mining uses AI, ML, and data mining technologies to mine personal information from unorganized and unstructured text such as emails, support chats, social media channels, forums, and blog comments. There is no need for manual data processing as algorithms use automatic, rule-based, or hybrid methods to churn out the sentiments. 

Grammarly as a Sentiment Analysis Tool

Besides being a tool to fix grammatical and punctuation mistakes, Grammarly is also capable of functioning as an opinion-mining tool. If you have used Grammarly integration on your email, you might have seen an emoji at the bottom of your email that marked your email content as friendly, formal, informal, etc.

This emoji shows the results of tone or sentiment analysis of your text. Grammarly uses a set of rules and machine learning to locate the signals in your writing that influence the tone or sentiment. It analyzes your words, capitalization, punctuation, and phrasing to tell you how the recipient will find it.

Apart from emails, it can detect the sentiment of any text you write and tell you the dominant sentiment of emotion included in that piece of writing. Using it, you can choose the right tone that will help you build healthy relationships with others.

Importance of Sentiment Analysis

Real-Time Sentiment Tracking

While acquiring new customers is costlier than keeping the existing ones, the latter also needs constant monitoring. What someone feels about your brand today might change tomorrow. Opinion mining lets you know their sentiment in real-time and immediately take action. 

Better Products and Services

Customer sentiment allows you to review customer responses and feedback. The data will help you develop better products and offer improved customer service. Also, it enhances your team’s productivity by quickly identifying sentiments and themes. 

Get Actionable Data

Sentiment analysis lets you get hold of actionable data. Social media these days are full of data as people keep talking about brands and tagging them. Analyzing these data for sentiment means knowing about your brand image and product performance.

Curated Marketing Campaigns

With opinion mining, you can assess your marketing campaigns. Its results enable you to take action as per the customer’s feelings. These insights help companies improve their marketing strategy. For example, you can run a special campaign for people interested in buying your products and have a positive notion about your company.

Brand Image Monitoring

The business world is so competitive nowadays that retaining your brand image is daunting. You can use opinion mining to determine how the customer perceives your company and take steps accordingly. 

Types of Sentiment Analysis

Depending on your company’s needs, you can perform any opinion mining model to capture various emotions.

Fine-Grained Analysis

This model is useful for deriving polarity precision. It helps you to study reviews and ratings you receive from your customers. Companies can apply this analysis across different following polarity categories such as highly positive, positive, negative, highly negative, or neutral. 

Aspect-Based Analysis

This type of sentiment analysis offers a deeper analysis of your customer reviews. It determines which aspects of business or ideas the customers are talking about. 

If you are a fruit juice seller and received a review that says, “Refreshing, but should include a  bio-degradable straw.” This analysis will find out that it talks positively about your juice but negatively about the packaging.

Emotion Detection Analysis

Using this model, organizations can detect the emotions included in user feedback, such as anger, satisfaction, frustration, fear, worry, happiness, and panic. This system usually uses lexicons, while some advanced classifiers also use machine learning algorithms.

However, to detect emotions, you should use Machine learning over lexicons. One word can convey positive or negative meaning based n its use. While the lexicon might detect the emotion inaccurately, ML can rightly determine the emotions.

Intent Analysis

Using this model, you can determine consumer intent accurately. As a result, you do not have to spend time and effort after the audience who does not intend to buy anything soon. Instead, you get to focus on customers who are planning to buy your products. You can use retargeting marketing to get their attention.

How Does Sentiment Analysis Work?

Opinion mining usually works via an algorithm that scans the sentences and decides whether it is positive, neutral, or negative. Advanced opinion mining tools replace the static or conventional algorithm with artificial intelligence and machine learning. Hence, industry people also refer to opinion mining as emotion AI.

Sentiment analysis currently follows the following two working models:

#1. Machine Learning Sentiment Analysis

As the name suggests, this technique utilizes ML and natural language processing (NLP) to learn from various training inputs. Hence, the model’s accuracy highly depends on the input content quality and proper understanding of the sentiment of sentences. More on that is below in the “How to Create Sentiment Analysis Using Machine Learning” section.     

#2. Rule-Based Sentiment Analysis

It is the conventional way of opinion mining. The algorithm has some preset rules for identifying sentiment for any sentence. A rule-based system also utilizes NLP manually through the list of words (lexicons), tokenization, parsing, and stemming.

Here is how it works:

A Library of Lexicons

The programmer creates a library of positive and negative words inside the algorithm. One can use any standard dictionary to do that. Here, it would help if you were careful when deciding which are positive or negative words. If you make any mistake, the output will be flawed.

Tokenization of Texts

Since machines can not understand human spoken language, programmers need to split the texts into the smallest possible fragment, like words. Hence, there is sentence tokenization that splits texts into sentences. Similarly, word tokenization splits up the terms of a sentence.

Removal of Unnecessary Words

Lemmatization and stopword removal play a major role at this point. Lemmatization is the grouping of similar words in one group. For example, Am, Is, Are, Been, Were, etc., are considered “be.”

Similarly, stopword removal removes excess words like For, To, A, At, etc., that do not make any significant changes in terms of sentiment in the text.

Computerized Counting of Sentiment Words

Since you will be analyzing terabytes of texts in a sentiment analysis project, you need to use a computer program to count all the positive, negative, and neutral words efficiently. It also helps in mitigating any human errors in the process.

Calculating Sentiment Score

Now, the task of opinion mining is simple. The program needs to give a score to the text. The score could be in percentage form, like 0% is negative, 100% is positive, and 50% is neutral.

Alternatively, some programs use the -100 to 100 scale. In this scale, 0 is neutral, -100 is negative, and 100 is positive sentiment.

Real-Life Applications of Sentiment Analysis

Companies keep gathering qualitative data that needs to be analyzed correctly. The real-life use cases of opinion mining are:

• Sentiment analysis is used to analyze customer support conversations. It helps businesses streamline their workflow and improve their customer service experience.
• What customers say on forums and online communities bears significance for companies. They use this method to understand the overall customer impression on those platforms.
• Customer reviews on social media can make or break a business. Sentiment analysis is often used to identify what the audience says about a company.
• Opinion mining can identify market trends, determine new markets, and analyze competitors. Hence people use it for market research before launching new products or brands.
• Product review is another arena where companies use sentiment analysis. Thus, businesses know where they can improve on their products. 
• Surveys on a newly launched product or a beta version of an app contain information you can use to improve the product. Opinion mining is also helpful in gathering crucial data from customer surveys.

Create Sentiment Analysis Using Machine Learning

Pre-Processing of Texts

In text pre-processing, an ML algorithm may utilize stopword removal and lemmatization to remove non-critical words that do not play any role in AI mining. 

After processing raw text, the AI program applies a vectorization method to transform the sentiment words into numerics. The industry term for this numeric representation of words is Features.

Bag-of-n-grams is the common way for vectorization. However, deep learning has made many advancements in this field and introduced the word2vec algorithm that utilizes a neural network.     

Training the AI and Prediction

The AI trainer needs to feed a set of sentiment-labeled training data. The data mainly includes many pairs of Features. Pairs of Features means a numeric representation of a sentiment word and its corresponding label: negative, neutral, or positive.  

Prediction of Real-Life Text

Now, the programmer would feed unseen or new text into the ML system. It will use its learning from training data to generate tags or classes for unseen texts.

Sometimes, an AI system can also utilize classification algorithm models like Logistic Regression, Naive Bayes, Linear Regression, Support Vector Machines, and Deep Learning.  

Opinion Mining Tools

Now that you know about the concept of sentiment analysis in detail, it is time to find out about the top opinion mining tools.

MonkeyLearn

MonkeyLearn is a Sentiment Analyzer software that can quickly detect emotions in unorganized text data. Using this tool, companies can find out promptly about the negative comments and respond instantly to build a positive impression. 

You can monitor customers’ thoughts of your products, services, or brand. Thus, response time to urgent queries for your company also increases to a large extent. It also lets you visualize sentiment insights.

MonkeyLearn supports integration with hundreds of applications for text analysis, including Zapier, Airtable, Gmail, Intercom, MS Excel, Google Sheets, Zendesk, SurveyMonkey, Typeform, and Service Cloud. 

Awario

If you are looking for a reliable sentiment analysis tool to track social listening, Awario is the application for you. It measures the sentiment built around your brand and how it changes over time so you can understand your reputation. 

Using this tool, you can spot negative social media comments and reply to them on a priority basis. It informs you of your customer’s reactions to your marketing campaigns and newly-released products. 

Moreover, businesses can use this platform to analyze their competitors to identify their strengths and weaknesses. You can also get the analysis stats in PDF format and share them with others.

Thematic

Thematic is a feedback analytics platform that you can use for sentiment analysis as well. It offers you complete insights into your customers using AI-driven opinion mining. Using this tool, you can understand customer feedback on a central platform and prioritize your responses.

This platform collects feedback from surveys, social media, support chats, open-ended customer responses, and reviews. Then, it categorizes them into different themes and sentiments using AI. 

Hence, you know what matters to customers. This platform does not need training or manual coding as you can seamlessly understand the trending themes among the customers.

Final Words

The customer sentiment and purchase intent go hand to hand. Companies can design their marketing plan by knowing the positive or negative impression of their potential and existing customers. Sentiment analysis also helps you with social media management and company branding.

Now that you know the importance of opinion mining and how it works, you can implement this method into your business with the help of the top sentiment analyzers. You may also create a sentiment analysis solution using Machine Learning. 

If interested, check out this list of customer feedback tools to improve your products.