Sentiment Analysis: Concept, Analysis, Tools, & Applications

Text analysis and natural language processing (NLP) are now being used by organizations to break down the complex unstructured text. One of the best examples of this is sentiment analysis. It helps businesses analyze what is being said about them and what is public opinion.

In order to be successful, businesses must pay attention to how and what their customers are saying in order to comprehend their thoughts. When customers mention your brand in online comments, reviews, tweets, or on social media platforms, you can use sentiment analysis to determine how they feel about your brand or its products and services.

In this article, let’s discuss what sentiment analysis is, how it works, and how you can leverage it to know what your customers think about you. Let’s dive in.

Customers’ positive or negative feelings about a product or service are assessed using a technique known as sentiment analysis. Businesses can gain insight into their consumers’ social sentiment by monitoring online conversations.

Sentiment analysis has gained much importance as a means of monitoring and understanding online interactions as customers are more eager than ever about sharing their experiences with the brand. You could understand what makes your customers happy or unsatisfied by automatically analyzing customer comments and reviews from surveys or social media interactions. This study can also be used to modify your products and services to match your customers’ requirements and help you build a strong brand.

Sentiment analysis algorithms have become more effective thanks to recent developments in machine learning and deep learning. Research and analysis can be enhanced with the use of cutting-edge NLP and machine learning methods. You can automate the analysis of thousands of reviews to analyze public opinion and evaluate brand perception.

The types of sentiment analysis can be classified based on how you analyze your customer feedback compiled from various sources.

Fine-grained sentiment analysis

This type of sentiment analysis is very similar to the 5-start feedback rating system. It analyzes the customer feedback and assigns a score between 1-5. The score is then assigned to the pre-defined polarity categories such as: very positive, positive, neutral, negative, and very negative.

Similar to 5-start ratings, the lowest score signifies very negative feedback and the highest score signifies very positive feedback.

Aspect-based sentiment analysis

This type of sentiment analysis applies when you are trying to analyze customer sentiments regarding the features of your products. It can help you analyze which aspects or features of your product the customers talk about the most and in what way. Is the customer feedback positive, neutral, or negative?

For example, The camera quality of this smartphone is terrible, or, the battery backup of this smartphone is the best. These comments offer negative and positive sentiments about 2 different features of a smartphone.

Emotion detection

This sentiment analysis approach aids in the detection of client emotions such as delight, disappointment, fury, and grief. To determine the emotion represented in client feedback, you can use sentiment lexicons or advanced machine learning approaches.

However, there are several disadvantages to employing lexicons. One of the most crucial is that people communicate their feelings in a variety of ways, making it difficult to understand what they meant. Sarcasm is a prime example of this.

Multilingual sentiment analysis

Multilingual sentiment analysis can be difficult since it requires a substantial amount of pre-processing. It necessitates a significant amount of preprocessing and resources. Most of the materials are available online, although others, such as noise detection and translated corpora, require further work. To create these resources, you must also have some coding skills.

To detect the language in the text, you should instead use a language classifier. Then, in the language of your choice, train bespoke sentiment analysis models to classify unstructured text.

The most important benefit of sentiment analysis is that it helps you to learn about your clients’ feelings about your brand or any of its products and services. The analysis will help enhance your products and services so they can better fulfill the needs and expectations of customers.

Analyze large volumes of unstructured text

Only 10% of the world’s data is organized according to the survey. Businesses generate a ton of data including emails, support tickets, customer support chats, social media interactions, feedback, and more. It’s difficult to quickly examine such large volumes of data. Although, with the help of sentiment analysis you can automate the analysis of the data and extract valuable insights with ease.

Consistent data analysis criteria

Manual textual data analysis is very subjective because it is heavily impacted by one’s own views and beliefs, as well as one’s own individual experience. There is no way to ensure that the standards used for analysis are consistent throughout the project. The automatic sentiment analysis tool helps you predefine the criteria and filters for all your data, improving their accuracy and providing you with better insight.

Perform real-time analysis

Have customers decided to quit because they were inadvertently ignored? Is there a social media meltdown? To avoid such problems, you must be quick to respond so that they do not escalate. However, because the volume of data is so large, it is difficult to discover and respond to each issue in a timely manner. Sentiment analysis facilitates the identification of such situations and increases reaction time. It will assist you in retaining departing consumers as well as avoiding social media crisis scenarios.

Here are the various sentiment analysis techniques that you can use to build a sentiment analysis model of your own.

Rule-Based Approach

In order to determine polarity, subjectivity, or the subject of a comment, the rule-based method uses sentiment methods that rely on manually designed rules. Computational linguistics techniques such as stemming tokenization, parsing, lexicons, or part of speech tagging are a part of the

Think of two lists of polarized words, such as “terrible” and “excellent”. The number of positive and negative terms in the text must be tallied. When there are more positive terms in the text than negative, the text is considered more upbeat and vice versa if there are more downbeat words. It is neutral if the number of negative and positive terms are identical.

The rule-based system is rather unsophisticated because it does not take into account the structure of words in the sequence.

Automatic Approach

The automatic approach relies on machine learning techniques rather than rules. In this case, the sentiment analysis system is a classification problem where the input is the text that will be evaluated. If the text is positive, negative, or neutral, it will return a polarity.

Preparation and Training

An input (text) is linked to an appropriate output (another text) by using a test sample during training. The input will be transferred to the feature vector with the help of the feature extractor. An algorithm is used to build an accurate model using these feature vectors and the tags provided.

Unknown text inputs are transformed into feature vectors by the extractor in the prediction process. The anticipated tags like positive, negative, and neutral are also generated by these feature vectors.

Extraction of Feature Sets from Textual Data

Text extraction utilizing the traditional bag-of-words or bag-of-n-grams approach will be transformed by machine learning text classifiers. Word vectors, which are word embeddings, are used to construct a new feature extraction approach.

Words having similar meanings can be represented in a more comparable way, which aids classifiers in their performance.

Important Classification Algorithms

• Naïve Bayes: The Bayes theorem is used to predict the category of a text using a series of probabilistic algorithms.
• Linear Regression: Predicts some value (y) for a given feature set using a range of statistical techniques (x).
• Support Vector Machines: It is a non-probabilistic method that represents the input text as a point in multidimensional space. New texts are classified into categories based on how closely they resemble existing ones and the areas they map.
• Deep Learning: An algorithmic family that uses artificial neural networks to analyze data to resemble the human brain.

Hybrid Approach

The rule-based and automatic approaches are merged into a single system in the hybrid model. In many cases, the outcomes are more accurate and precise than those produced by rule-based and automatic methods.

Sentiment Analysis Machine Learning Approach

While the above-mentioned approaches are good, they are difficult to elaborate on. But if you add machine learning you can develop a sentiment analysis model more effectively by creating vector representations of words.

Word Vectors

Using vectors to represent text considers not only the individual words but also the overall structure of the text. It is essential that you define each word with an n-feature vector and use a n*length matrix to describe the phrase.

Tomas Mikolov devised a brand-new method for representing words on a graphical plane. A large corpus of data is used to train the neural network model, and the output vector from that layer is used to define the term “ants.” To study the statistical distribution of a word following “ant” in a phrase, word vectors capture the semantic information.

An index vector represents each word, and this is exactly how it’s done here. An input to a convolution neural network is this little vector. These large vectors are further divided and put into the deep learning model as a hidden layer of linear neurons.

It is, therefore, necessary to train the model in its entirety in order to ensure that the word vectors you employ are sufficient to suit the sentiment information of the word, in other words, the features you receive capture enough data on each term to forecast its sentiment.

Feature Extraction

In order to develop a deep learning sentiment analysis model, you first need to encode your phrases in a vector space. Methods that use frequency components to represent a phrase use either a bag-of-words or a term frequency-inverse document frequency vector.

In the case of lengthy texts, it is advantageous to use these strategies. Newspaper and book articles and books can be efficiently classified by their most frequently used words. For example, shorter sentences may not yield as accurate results as longer ones.

As a result, tf-IDF models fail to account for the negations, concessions, and amplifications that are inherent in the sentence structure. “Good camera, but bad battery life.” has the same impact as “Bad camera, but excellent battery life.” for example.

Sentence Representation

It is necessary to construct a sentence representation that considers the text’s words and semantic structure. When creating a matrix of word vectors that represent the text, it is easiest to superimpose these word vectors.

Tomas Mikolov claims that the Doc2Vec method can also be used to accomplish this. In this case, he adjusts the Word2Vec neural network to accept input in the form of a word vector and a sentence-dependent vector.

Gradient descent is used to improve the model’s parameters, including the word vector. When you do this, you’ll have a range of features for each phrase that describes the sentence structure.

As previously said, the wording of customer reviews might indicate whether the customer has a pleasant or negative experience. A fine-grained classification of sentiment is one of the greatest sentiment analysis approaches, especially when utilizing logistic regression to feed these vectors and train it to predict sentiments from the text document.

Creating the document vector for the provided text is no small feat. A gradient descent technique must be used to get the correct coefficient for this vector in each sentence. As a result, a longer preprocessing technique is required for Doc2Vec classification, which means it is more expensive and takes longer to process than other methods of sentiment analysis.

Here are a few challenges that sentiment analysis models face while analyzing textual data.

Context & Polarity

Analyzing sentiment without context is challenging since machines cannot learn about contexts unless deliberately instructed to do so. Changing polarity is the most significant drawback of context.

Take a moment to think about the question, “What do you like about this phone?” There will be two reactions: one favorable and one negative. Is there anything about this phone that didn’t meet your expectations? In the query, the negative verb “dislike” changes the text’s sentiment analysis.

Several preprocessing and postprocessing procedures will be required if you are looking at the tiniest fraction of the context in the input text.

Irony & Sarcasm

It’s tough for computers to grasp sarcasm because individuals often use affirmative language to describe their negative feelings, making it harder for them to understand what they’re saying and how they’re feeling.

“Did you enjoy your vacation with us?”

Yes, of course. It’s so much fun!

There is a slew of them!

Isn’t it correct that the exclamation point shows negation in response to the first question? Because “yep” and “sure,” which are often deemed positive or neutral, there is no textual signal to help the algorithm grasp the emotion.

Consider, however, the second possibility. As a result, you will acquire a wide range of settings in which negative sentiment might be conveyed.

Subjectivity & Tone

The subjective and objective text might be found in the same document. In contrast to objective texts, which do not express their feelings explicitly, subjective texts do. Here are some examples of texts that you might want to analyze:

It’s a nice device.

It’s a red device.

For the first, you’ll remark that attitudes are favorable, whereas, for the second, you’ll say that attitudes are neutral. Text predicates should not be viewed any differently in this context in terms of how they evoke emotion. Nice is a more evaluative term than blue in this case.

Emojis

Western emojis (:D) have only one or two characters, while Eastern emojis (¯ \ (ツ) / ¯) contain more characters of a vertical nature.

When it comes to sentiment analysis, emojis play an important role, particularly when dealing with tweets. For tweet analysis, you’ll need to pay closer attention at both the character and word levels. And it’s possible that a significant amount of preprocessing will be necessary.

Pre-processing the tweets, for example, requires converting the eastern emoticons and the western emojis in the tweets into tokens Additionally, adding them to your whitelist will enhance the accuracy of your sentiment analysis.

Neutral Sentiments

The most difficult part of reliable sentiment analysis is defining the category of neutral. Classifying sentiment analysis into positive, negative, and neutral is a necessary first step, as previously noted. Finding the neutral tag is the most crucial and difficult challenge in this scenario. It is essential to have a clear definition of the problem in order to get accurate findings when tagging data.

Human Annotator Accuracy

Both humans and machines have their challenges when it comes to performing Sentiment Analysis. Sentiment analysis has a low level of inter-annotator agreement. For this reason, sentiment analysis classifiers are less accurate than other types of classification systems.

Customer Service

A common practice among customer service organizations is to employ sentiment analysis to categorize incoming calls into two categories: “urgent” and “not urgent.” Classification is based on the tone and content of emails and phone calls from dissatisfied clients.

Customers want a seamless, one-on-one, and immediate relationship with the businesses they do business with. In order to maintain their brand value, service providers focus more on answering urgent calls. Assessing the quality of your customer service encounters can help you determine whether your staff is following the proper procedures. Additionally, make your services more effective so that clients aren’t left waiting for prolonged periods of time for assistance.

Thanks for reaching out, Hillary. We’re so sorry for the trouble with these charges. Please shoot us a DM with your email on file and we’d be happy to look into this further for you. Thanks in advance!

— ClassPass (@classpass) May 5, 2022

Understanding the feelings of customers has never been more important than it is now, thanks to the increasing use of machine learning in customer care. Many firms are turning to chatbots as their first line of customer service because they can better understand the conversation’s context and intent.

Brand Monitoring

An organization’s reputation is built on more than just its products. Online marketing, social media campaigns, content marketing, and customer service all play a role. One of the most critical applications of sentiment analysis is to get a complete picture of how your customers see your product, organization, or brand.

Using sentiment analysis, you may get a sense of what your potential customers are thinking. With the use of social media and surveys, you can learn a lot about what your customers think of your company.

Sophisticated businesses are increasingly relying on sentiment analysis as a strong tool to gauge the success of their goods and campaigns. Brand monitoring provides a lot of information on your brand’s conversions in the marketplace. All mentions of a brand can be automatically categorized and routed to the appropriate team using sentiment analysis.

It is possible to design more appealing branding approaches and marketing plans if you keep the input of your customers in mind.

Business Intelligence

In today’s commercial world, digital marketing plays a significant role. The product’s reaction and reviews are frequently shared on social media. Having sentiment data about your firm and new goods might help you estimate your client retention rate more accurately.

When it comes to determining how your product is doing in the market, sentiment analysis can help. As an additional resource, look at the feedback your competitors have provided.

You can better assist your clients based on the results of the survey. Decisions that are made now will help you adapt to current market conditions.

Keeping up with the times is the key to business intelligence. Because of this, sentiment analysis allows you the freedom to manage your firm successfully. For instance, it’s possible to test and assess your ideas before putting them into action.

Finance & Stocks

According to the saying, “Be greedy when others are afraid, and terrified when others are greedy.” But how do you tell if others are afraid or greedy?

This is a good place to put the sentiment analysis technique to use. Even in the corporate realm, making investments can be a challenge. Investing in stocks and the stock market is always a risk, but it can be reduced if you conduct thorough research before making a decision.

This is inaccurate. Tesla only sold ~10% of holdings to confirm BTC could be liquidated easily without moving market.

When there’s confirmation of reasonable (~50%) clean energy usage by miners with positive future trend, Tesla will resume allowing Bitcoin transactions.

— Elon Musk (@elonmusk) June 13, 2021

Think about how customers feel about a firm’s most recent items if you’re considering investing in the automotive industry and aren’t sure which company X or company Y to go with. You’ll be able to identify which one is doing better in the market by using this information.

Market Research

Sentiment analysis is used in business intelligence to discover why consumers are or aren’t engaging with a product, user experience, or customer service.

You will be able to do market research and competition analysis using sentiment analysis. Whether you’re looking to break into a new market or get an advantage over the competition, it makes a big difference.

You can compare your goods to the ones of your competitors online. You can utilize your competitors’ weaknesses to your advantage as well.

Social and political science disciplines employ sentiment analysis to examine patterns in opinion as well as the ideological biases of individuals. Many of these applications for sentiment analysis are already in operation.

You have two options when it comes to sentiment analysis tool or text analysis in general: develop your own solution or subscribe to one.

Natural language processing and deep learning are popular forms of data science in these circles. You may easily develop your sentiment analysis solution using open-source libraries for languages like Python and Java. A data science and engineering staff, as well as substantial resources, are all requirements.

It only takes a few minutes and a few easy steps to conduct a sentiment analysis using the software as a service solution. A data science or engineering team can help you get up and running quickly, or you can forego coding altogether and implement AI with no or minimal coding.

The BytesView Zendesk, Excel, and Zapier Integrations are great examples of SaaS solutions where you don’t need to know how to code (for example).

Our guide to the top SaaS tools for sentiment analysis lists all the relevant APIs so that you can quickly integrate your existing software into these cutting-edge solutions.

BytesView’s API and pre-built sentiment analysis models may help you get started with sentiment analysis in just six lines of code. You can also use your own industry-specific data to train your own sentiment analysis models.

Sentiment analysis has the potential to improve a wide range of business processes, including brand monitoring, product analytics, customer service, and market research. Leading companies are always on the lookout for new ways to work more quickly and precisely.

For enterprises, sentiment analysis has gone from a fun technology to a crucial necessity. As a result, we can better serve our customers and workers by using sentiment analysis.

Using BytesView, you can process enormous amounts of data and derive meaningful insights quickly and easily. It also gives you the ability to create unique solutions for your business. Click here for a demo.