Use-cases of Sentiment Analysis in Machine Learning

Sentiment analysis (or opinion mining) may be a natural processing technique want to determine whether data is positive, negative, or neutral. Sentiment analysis is usually performed on textual data to assist businesses to monitor brand and merchandise sentiment in customer feedback and understand customer needs.

What Is Sentiment Analysis?

Sentiment analysis is that the process of detecting positive or negative sentiment in text. It’s often employed by businesses to detect sentiment in social data, gauge brand reputation, and understand customers.

Since customers express their thoughts and feelings more openly than ever before, sentiment analysis is becoming an important tool to watch and understand that sentiment. Automatically analyzing customer feedback, like opinions in survey responses and social media conversations, allows brands to find out what makes customers happy or frustrated, so that they will tailor products and services to satisfy their customers’ needs.

For example, using sentiment analysis to automatically analyze 4,000+ reviews about your product could assist you to discover if customers are happy about your pricing plans and customer service.

Maybe you would like to measure brand sentiment on social media, in real-time and over time, so you'll detect disgruntled customers immediately and respond as soon as possible.

The applications of sentiment analysis are endless. Learn more about how you'll out sentiment analysis to use afterward during this post.

Types of Sentimental Analysis:

Sentiment analysis models specialize in polarity (positive, negative, neutral) but also on feelings and emotions (angry, happy, sad, etc), urgency (urgent, not urgent), and even intentions (interested v. not interested).

Depending on how you would like to interpret customer feedback and queries, you'll define and tailor your categories to satisfy your sentiment analysis needs. within the meantime, here are a number of the foremost popular sorts of sentiment analysis:

1. Fine-grained Sentiment Analysis

If polarity precision is vital to your business, you would possibly consider expanding your polarity categories to include:

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

This is usually mentioned as fine-grained sentiment analysis, and will be wont to interpret 5-star ratings during a review, for example:

  • Very Positive = 5 stars
  • Very Negative = 1 star
2. Emotion detection

This type of sentiment analysis aims to detect emotions, like happiness, frustration, anger, sadness, and so on. Many emotion detection systems use lexicons (i.e., lists of words and therefore the emotions they convey) or complex machine learning algorithms.

One of the downsides of using lexicons is that folk express emotions in several ways. Some words that typically express anger, like bad or kill (e.g., your product is so bad or your customer support is killing me) may additionally express happiness (e.g., this is often badass otherwise you are killing it).

3. Aspect-based Sentiment Analysis

Usually, when analyzing sentiments of texts, let’s say product reviews, you’ll want to understand which particular aspects or features people are mentioning in a positive, neutral, or negative way. That's where aspect-based sentiment analysis can help, for instance during this text: "The battery life of this camera is just too short", an aspect-based classifier would be ready to determine that the sentence expresses a negative opinion about the feature battery life.

4. Multilingual sentiment analysis

Multilingual sentiment analysis is often difficult. It involves tons of pre-processing and resources. Most of those resources are available online (e.g., sentiment lexicons), while others got to be created (e.g., translated corpora or noise detection algorithms), but you’ll get to skills to code to use them.

Alternatively, you'll detect the language in texts automatically with a language classifier, then train a custom sentiment analysis model to classify texts within the language of your choice.

Why Is Sentiment Analysis Important?

Sentiment analysis is extremely important because it allows businesses to know the sentiment of their customers towards their brand. By automatically sorting the sentiment behind social media conversations, reviews, and more, businesses can make better and more informed decisions.

It’s estimated that 90% of the world’s data is unstructured, in other words, it’s unorganized. Huge volumes of unstructured business data are created every day: emails, support tickets, chats, social media conversations, surveys, articles, documents, etc). But it’s hard to research sentiment in a timely and efficient manner.

The overall benefits of sentiment analysis include:

1. Sorting Data at Scale

Can you imagine manually sorting through thousands of tweets, customer support conversations, or surveys? There’s just an excessive amount of business data to process manually. Sentiment analysis helps businesses process huge amounts of knowledge efficiently and cost-effectively.

2. Real-Time Analysis

Sentiment analysis can identify critical issues in real-time, for instance, maybe a PR crisis on social media escalating? Is an angry customer close to churning? Sentiment analysis models can assist you in immediately identify these sorts of situations, so you'll take action directly.

3. Consistent criteria

It’s estimated that folks only agree around 60-65% of the time when determining the sentiment of a specific text. Tagging text by sentiment is very subjective, influenced by personal experiences, thoughts, and beliefs. By employing a centralized sentiment analysis system, companies can apply an equivalent criterion to all or any of their data, helping them improve accuracy and gain better insights.

How Does Sentiment Analysis Work?

Sentiment analysis, otherwise referred to as opinion mining, works because of tongue processing (NLP) and machine learning algorithms, to automatically determine the emotional tone behind online conversations.

There are different algorithms you'll implement in sentiment analysis models, counting on what proportion of data you would like to research, and the way accurate you would like your model to be. We’ll re-evaluate a number of these in additional detail, below.

Sentiment analysis algorithms fall under one among three buckets:

  • Rule-based: these systems automatically perform sentiment analysis supported by a group of manually crafted rules.
  • Automatic: systems believe in machine learning techniques to find out from data.
  • Hybrid systems combine both rule-based and automatic approaches.

Rule-based Approaches

Usually, a rule-based system uses a group of human-crafted rules to assist identify subjectivity, polarity, or the topic of an opinion.

These rules may include various NLP techniques developed in linguistics, such as:

Stemming, tokenization, part-of-speech tagging, and parsing.

Lexicons (i.e., lists of words and expressions).

Here’s a basic example of how a rule-based system works:

Defines two lists of polarized words (e.g., negative words like bad, worst, ugly, etc, and positive words like good, best, beautiful, etc).

Counts the amount of positive and negative words that appear during a given text.

If the amount of positive word appearances is bigger than the number of negative word appearances, the system returns a positive sentiment and the other way around. If the numbers are even, the system will return a neutral sentiment.

Rule-based systems are very naive since they do not take into consideration how words are combined during a sequence. Of course, more advanced processing techniques are often used, and new rules are added to support new expressions and vocabulary. However, adding new rules may affect previous results, and therefore the whole system can get very complex. Since rule-based systems often require fine-tuning and maintenance, they’ll also need regular investments.

Automatic Approaches

Automatic methods, contrary to rule-based systems, don't believe in manually crafted rules, but in machine learning techniques. A sentiment analysis task is typically modeled as a classification problem, whereby a classifier is fed a text and returns a category, e.g., positive, negative, or neutral.

The Training and Prediction Processes

In the training process (a), our model learns to associate a specific input (i.e., a text) to the corresponding output (tag) supported by the test samples used for training. The feature extractor transfers the text input into a feature vector. Pairs of feature vectors and tags (e.g., positive, negative, or neutral) are fed into the machine learning algorithm to get a model.

In the prediction process (b), the feature extractor is employed to rework unseen text inputs into feature vectors. These feature vectors are then fed into the model, which generates predicted tags (again, positive, negative, or neutral).

Feature Extraction from Text

The first step during a machine learning text classifier is to rework the text extraction or text vectorization, and therefore the classical approach has been bag-of-words or bag-of-n-grams with their frequency.

More recently, new feature extraction techniques are applied supported word embeddings (also referred to as word vectors). this type of representation makes it possible for words with similar aiming to have an identical representation, which may improve the performance of classifiers.

Classification Algorithms

The classification step usually involves a statistical model like Naïve Bayes, Logistic Regression, Support Vector Machines, or Neural Networks:

Naïve Bayes: a family of probabilistic algorithms that uses Bayes’s Theorem to predict the category of a text.

Linear Regression: a well-known algorithm in statistics that wants to predict some value (Y) given a group of features (X).

Support Vector Machines: a non-probabilistic model which uses a representation of text examples as points during a multidimensional space. samples of different categories (sentiments) are mapped to distinct regions within that space. Then, new texts are assigned a category supported similarities with existing texts and therefore the regions they’re mapped to.

Deep Learning: a various set of algorithms that plan to mimic the human brain, by employing artificial neural networks to process data.

Hybrid Approaches

Hybrid systems combine the desirable elements of rule-based and automatic techniques into one system. One huge advantage of these systems is that results are often more accurate.

Sentiment Analysis Challenges

Sentiment analysis is one of the toughest tasks in tongue processing because even humans struggle to research sentiments accurately.

Data scientists are becoming better at creating more accurate sentiment classifiers, but there’s still extended thanks to going. Let’s take a better check out a number of the most challenges of machine-based sentiment analysis:

Subjectivity and Tone

There are two sorts of text: subjective and objective. Objective texts don't contain explicit sentiments, whereas subjective texts do. Say, for instance, you plan to research the sentiment of the subsequent two texts:

The package is good.

The package is red.

Most people would say that sentiment is positive for the primary one and neutral for the other, right? All predicates (adjectives, verbs, and a few nouns) shouldn't be treated as equivalent concerning how they create the sentiment. within the examples above, nice is more subjective than red.

Context and Polarity

All utterances are uttered at some point in time, in some place, by and to some people, you get the purpose. All utterances are uttered in context. Analyzing sentiment without context gets pretty difficult. However, machines cannot study contexts if they're not mentioned explicitly. one of the issues that arise from context is changes in polarity. check out the subsequent responses to a survey:

a. Everything of it.

b. Nothing!

Imagine the responses above come from answers to the question What did you wish about the event? the primary response would be positive and therefore the other would be negative, right? Now, imagine the responses come from answers to the question What did you dislike about the event? The negative within the question will make sentiment analysis change altogether.

A good deal of pre-processing or postprocessing is going to be needed if we are to require under consideration a minimum of a part of the context during which texts were produced. However, the way to pre-process or post-process data to capture the bits of context which will help analyze sentiment isn't straightforward.

Irony and Sarcasm

When it involves irony and sarcasm, people express their negative sentiments using positive words, which may be difficult for machines to detect without having a radical understanding of the context of things during which a sense was expressed.

For example, check out some possible answers to the question, did you enjoy your shopping experience with us?

a. Yeah, sure. So smooth!

b. Not one, but many!

What sentiment would you assign to the responses above? the primary responsibility with an exclamation point might be negative, right? the matter is there's no textual cue that will help a machine learn, or a minimum of question that sentiment since yeah and sure often belong to positive or neutral texts.

How about the second response? during this context, sentiment is positive, but we’re sure you'll come up with many various contexts during which an equivalent response can express negative sentiment.


How to treat comparisons in sentiment analysis is another challenge worth tackling. check out the texts below:

a. This product is second to none.

b. This is better than older tools.

c. This is better than nothing.

The first comparison doesn’t need any contextual clues to be classified correctly. It’s positive.

The second and third texts are a touch harder to classify, though. Would you classify them as neutral, positive, or maybe negative? once more, context can make a difference. for instance, if the ‘older tools’ within the second text were considered useless, then the second text is pretty almost like the third text.


There are two sorts of emojis consistent with Guibon et al. Western emojis (e.g., :D) are encoded in just one or two characters, whereas Eastern emojis (e.g., ¯ \ (ツ) / ¯) is an extended combination of characters of a vertical nature. Emojis play a crucial role within the sentiment of texts, particularly in tweets.

You’ll get to pay special attention to character-level, also as word-level when performing sentiment analysis on tweets. tons of pre-processing may additionally be needed. for instance, you would possibly want to pre-process social media content and transform both Western and Eastern emojis into tokens and whitelist them (i.e. always take them as a feature for classification purposes) to assist improve sentiment analysis performance.

Here’s a quite comprehensive list of emojis and their Unicode characters which will are available handy when pre-processing.

Defining Neutral

Defining what we mean by neutral is another challenge to tackle to perform accurate sentiment analysis. As altogether classification problems, defining your categories -and, during this case, the neutral tag- is one among the foremost important parts of the matter. What you mean by neutral, positive, or negative does matter once you train sentiment analysis models. Since tagging data requires that tagging criteria be consistent, an honest definition of the matter may be a must.

Here are some ideas to assist you to identify and define neutral texts:

  1. Objective texts. So-called objective texts don't contain explicit sentiments, so you ought to include those texts into the neutral category.
  2. Irrelevant information. If you haven’t pre-processed your data to filter irrelevant information, you'll tag it neutral. However, be careful! Only do that if you recognize how this might affect overall performance. Sometimes, you'll be adding noise to your classifier and performance could worsen.
  3. Texts containing wishes. Some wishes like I wish the merchandise had more integrations are generally neutral. However, those including comparisons like I wish the merchandise were better are pretty difficult to categorize

Human Annotator Accuracy

Sentiment analysis may be a tremendously difficult task even for humans. on average, inter-annotator agreement (a measure of how well two (or more) human labelers can make an equivalent annotation decision).is pretty low when it involves sentiment analysis. And since machines learn from the info they're fed, sentiment analysis classifiers won't be as precise as other sorts of classifiers.

Still, sentiment analysis is well worth the effort, albeit your sentiment analysis predictions are wrong from time to time. By using the sentiment analysis model, you'll expect correct predictions about 70-80% of the time you submit your texts for classification.

If you're new to sentiment analysis, then you’ll quickly notice improvements. For typical use cases, like ticket routing, brand monitoring, and VoC analysis, you’ll save tons of your time and money on tedious manual tasks

Advantages of using sentiment analysis

By using sentiment analysis, you gauge how customers feel about different areas of your business without having to read thousands of customer comments directly.

If you've got thousands of feedbacks per month, one person can’t read all of those responses. By using sentiment analysis and automating this process, you'll easily drill down into different customer segments of your business and obtain a far better understanding of sentiment in these segments.

Disadvantages of using sentiment analysis

While sentiment analysis is beneficial, it's not an entire replacement for reading survey responses. Often, there are useful nuances within the comments themselves. Where sentiment analysis can assist you further is by identifying which of those comments you ought to read.

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