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The Role of Natural Language Processing (NLP) in Sentimental Analytics


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Every day, millions of online users express their views and attitudes through numerous channels by posting their opinions on product features, benefits, and value. This 'opinion' or sentiment data which is sometimes generated invisibly – often contain crucial data points that can be quite useful for organizations wanting to improve their client experience, products, or services.

The E-Commerce industry views social media advertising as a critical metric for success because it ensures that visitors spend a significant amount of time on the portal, searching for products they like, making purchases, posting positive reviews on social media about the products they bought, and returning to the portal for future purchases.

The ability to mine vast stores of unstructured social data for actionable insights, which is a daunting task that requires sophisticated NLP (Natural Language Processing), statistics, or machine learning methods to characterize and capture the sentiment value, is the key to e-commerce success with sentiment data.

What is Sentiment Analysis, and how does it work?

Sentiment analysis (also known as opinion mining) is a natural language processing (NLP) technique for determining whether data is positive, negative or neutral. It is frequently used on textual data to assist organizations for a variety of purposes. Simply put, sentiment analysis aids in determining a particular person’s attitude toward a topic. Sentiment analysis software sorts the data into positive, neutral, and negative categories.

Sentiment analysis, also referred as text mining that finds and extracts subjective information from the source material, allowing a company to understand better the social sentiment of its brand, product, or service while monitoring online conversations. However, most social media stream analysis is limited to simple sentiment analysis and count-based metrics. This is similar to only scratching the surface and missing out on high-value discoveries that are just waiting to be uncovered.

There are a variety of approaches and procedures for analysing sentiment, which vary depending on the demands of each organization. Machine learning and its subset, Deep learning algorithms are used to develop sentiment analysis models. These models are taught to discern whether a message is positive, negative, or neutral by feeding them millions of pieces of text. Sentiment analysis breaks down a communication into topic chunks and assigns a sentiment score to each topic.

Statistics or machine learning based on supervised or semi-supervised learning techniques are used at the most basic level of sentiment analysis. Humans are used to score a data set in supervised learning. Semi-supervised learning combines automated learning with occasional checks to ensure the system is doing its job correctly.

Deep learning is another method for performing sentiment analysis. Machine learning uses algorithms to train computers on large amounts of data so that they can act on what they've been taught and learnt. Instead of comprehending what information means, the system learns to identify it based on patterns, keywords, and sequences.

Types of Sentiment Analysis

Here are a few of the most common Sentiment Analysis techniques:

  • Standard Sentiment Analysis - It recognizes the general tone of a written text and categorizes it as positive, negative, or neutral, this is one of the most popular types of Sentiment Analysis.

  • Fine-Grained Sentiment Analysis - This method has a more elaborate polarity range and can be utilized by organizations to gain a better grasp of client sentiment/feedback. The responses are divided into five different sentiments, ranging from 5-stars to 1-star.

  • Aspect-Based Sentiment Analysis - This analysis goes deeper than fine-grained analysis in determining the overall polarity of your customer evaluations. It assists you in determining which features (attributes or components) of a product are being discussed.

Significance of Sentiment Analysis

First and foremost, sentiment analysis is critical because emotions and attitudes regarding a topic may be transformed into meaningful data in a variety of fields, including business and research.

Second, it saves time and effort because the sentiment extraction process is totally automated - the algorithm analyses the sentiment datasets, so human involvement is minimal.

Third, as artificial intelligence, deep learning, machine learning methodologies, and natural language processing technologies advance, it is becoming a more popular phenomenon.

Fourth, as technology advances, sentiment analysis will become more accessible and affordable to the general public as well as smaller businesses.

Finally, the tools are getting smarter all the time. The more data they are fed, the better and more accurate they get at extracting sentiment.

Let's take a closer look at how sentiment analysis can help:

  • Management of a Company's Image - Consumers use the Internet to discuss brands, products, and services and share their experiences and recommendations. Opinions and comments abound on social media platforms, product evaluations, blog articles, and discussion forums, can be mined to collect and analyze business data. Sentiment analysis can be used for brand monitoring to assess the web and social media chatter regarding a product, a service, a brand, or a marketing campaign when it comes to brand reputation management. Online research aids in determining a brand's reputation and how consumers perceive it. Businesses can use discussion forums, online review sites, news sites, blogs, Twitter, and other publicly available internet sources to learn about customers, media, and expert opinions about their products, services, marketing efforts, and brands. For PR professionals, brand monitoring is a crucial area of business, and sentiment analysis should be one of their go-to tools.

  • Feedback from Customers - Customers' views are analyzed via sentiment analysis by businesses. Consumers nowadays use their social media sites to express both positive and bad brand experiences. A sentiment analysis tool can detect positive mentions indicating strengths, as well as negative mentions indicating negative reviews and problems that consumers confront and write about online. Customer support benefits from a speedy response time because the mentions are identified so rapidly. This makes customer service more attentive and responsive in some circumstances because the customer support team is informed about any nasty remarks in real-time. Any errors must be reported as soon as possible to the support team. This makes managing the customer experience much easier and more pleasurable.

  • Market Research - Sentiment analysis provides a large amount of data, making it a valuable supplement to any market research project. Whether you're looking at entire markets, niches, sectors, products, their specific characteristics, or market buzz, sentiment research may offer you a wealth of information about what people like, dislike, and expect. All of this information allows you to conduct more focused market research, which improves the decision-making process.

  • Crisis Prevention - Brand24, for example, is a sentiment analysis tool that also serves as a media monitoring tool. They collect real-time mentions of predetermined keywords from websites, news sites, discussion forums, and other sources. PR professionals can use such a solution to receive real-time notifications about any unfavourable content that has surfaced on the Internet. When a firm notices a poor customer sentiment remark, it can move immediately to address the issue before it becomes a social media crisis.

  • Valuable Business Intelligence - Sentiment analysis data offers businesses useful and informative information – about the present and prospective clients, fresh business marketplaces and opportunities – from which they can develop meaningful plans. Client service representatives can utilize social media to help them uncover customer pain areas, values, and habits, which can then be used to generate targeted communications that cater to their specific needs and desires. To generate a complete picture, intelligence insights must be combined with human insights and other essential metrics.

  • Rejuvenate the Brand - Customer views of your organization and its aims are crucial to branding, and sentiment analysis allows you to quantify these perceptions. What are the opinions of current and potential customers on products and services, their consumer journey and experience, web content, marketing, and social campaigns? In a nutshell, the brand as a whole. Businesses that completely integrate sentiment research in the future will earn higher commercial value and a distinct competitive advantage.

  • Consistent Criteria - When it comes to determining the sentiment of a text, it's estimated that just 60-65 per cent of the time, people agree. Text sentiment tagging is a highly subjective process that is impacted by human experiences, thoughts, and beliefs. Companies can apply the same criteria to all of their data by adopting a centralized sentiment analysis system, which helps them enhance accuracy and generate better insights.

What is the Process of Sentiment Analysis?

The approach is based on natural language processing and machine learning algorithms that classify pieces of writing as positive, neutral, or negative.

Sentiment Analysis may employ a variety of algorithms:

1. Automatic

This method is entirely based on machine learning techniques and learns from the data it receives.

  • By inputting a vast amount of text documents with pre-tagged samples, data scientists train a machine learning model to detect nouns.

  • The model will learn what nouns "look like" using supervised and unsupervised machine learning approaches such as neural networks and deep learning.

  • Once the model is complete, the same data scientists can use the same training procedures to create other models to recognize different bits of speech.

There are two types of machine learning models:

Traditional Models - This method necessitates acquiring a dataset with examples for positive, negative, and neutral classes, processing the data, and then training the algorithm using the instances. These approaches are mostly used to determine text polarity. Because of its scalability, traditional machine learning algorithms such as Naïve Bayes, Logistic Regression, and Support Vector Machines (SVM) are commonly utilized for large-scale sentiment analysis.

Deep Learning Models – These include neural network models such as CNN (Convoluted Neural Network), RNN (Recurrent Neural Network), and DNN (Deep Neural Network) that produce more exact results than traditional models.

Deep Neural Network

One of the most significant advantages of this algorithm is the enormous amount of data it can evaluate – far more than a rule-based algorithm. Machine learning also assists data analysts in resolving difficult challenges created by natural language inconsistencies.

When it comes to drawbacks, the algorithm makes it tough to explain text analysis decisions, making it impossible to tell why a paragraph was labelled as good or bad.

2. Rule-Based

Following the preparation of the sentiment libraries, software developers write a set of recommendations ("rules") to assist the computer in evaluating the sentiment expressed toward a certain entity (noun or pronoun) based on its proximity to known positive and negative phrases (adjectives and adverbs).

  • This method relies on manually generated data classification rules.

  • To generate a score, this method uses dictionaries of words with positive or negative values to represent their polarity and sentiment strength. Expressions can also be used to offer additional functionality. Rule-based sentiment analysis algorithms can also be adjusted based on context.

How it works - It counts how many positive and negative words there are in a given text. It will provide a positive attitude if the number of positives exceeds the number of negatives. It will return a neutral sentiment if both are equal.

Data preprocessing steps in Rule based Sentiment Analysis:

  • Cleaning the Text - The special characters and digits from the text should be deleted in this phase. Python's regular expression operations library can be used.

  • Tokenization - It is the process of breaking down a large text into smaller tokens. It can be done at the sentence or word level (sentence tokenization) (word tokenization).

  • Enrichment – POS tagging - The process of transforming each token into a tuple with the type Parts of Speech (POS) tagging (word, tag). POS tagging is required for Lemmatization and to maintain the context of the word.

  • Stopwords removal - In English, stopwords are words that provide relatively little relevant information. As part of the text preparation, we need to get rid of them. Every language has a list of stopwords.

  • Obtaining the stem words (Lemmatization) - A stem is a component of a word that determines its lexical meaning. Stemming and lemmatization are two typical methods for getting root/stem words. The main distinction is that, because it merely chops off certain characters at the end, stemming frequently produces useless root words. Lemmatization produces meaningful root words, but it necessitates the use of POS tags on the words.

This approach is transparent and straightforward when it comes to the principles underpinning analysis.

Having said that, the method also has some drawbacks. Every word combination in a sentiment library must have a rule in a rules-based system. The creation and maintenance of these standards necessitates arduous manual effort.

Finally, rules will never be able to keep pace with the evolution of natural human language.

The old rules of grammar have been mangled by instant messaging, and no ruleset can account for every abbreviation, acronym, double-meaning, or mistake that may arise in any given text composition.

3. Hybrid

Machine learning and traditional rules are used in hybrid sentiment analysis systems to compensate for the shortcomings of each technique. For example, rules-based sentiment analysis can be an excellent technique to lay the groundwork for PoS tagging and sentiment analysis. However, as we've seen, these rule sets quickly outgrow their control. This is where machine learning may help by taking on the burden of difficult natural language processing tasks like understanding double meanings.

From low-level tokenization and syntax analysis to the highest-levels of sentiment analysis, most hybrid sentiment analysis systems mix machine learning with software rules to ease operations.

What is Sentiment Score?

Sentiment score is one method of assessing sentiment.

It's a grading system that represents the emotional depth of a text's emotions. Sentiment score recognizes emotions and assigns them sentiment ratings ranging from 0 to 10 (from the most negative to the most positive). The sentiment score simplifies the process of determining how customers feel.

Patent Data Analysis

Top Players

IBM tops the chart because it has spent years in the field. Its flagship, The IBM Watson NLU sentiment analysis tool, tells a user whether their data has a "positive" or "negative" sentiment and assigns a score to it. It is the most sought after tool in recent times. Unaddressed biases in machine learning models do not yield desirable or accurate outcomes, and a biased algorithm can produce stereotype-informed outputs. It's critical to train AI impartial, unbiased, and unwavering as artificial intelligence continues to automate corporate activities. Following IBM's footsteps is Cognitive scale is making a mark in the AI industry by providing organizations with intelligent, transparent, and trusted AI-powered digital systems. It also recently partnered with Ascendum to deliver Trusted AI solutions for the healthcare, fintech, and retail/eCommerce verticals. CognitiveScale will provide its trusted AI software, while Ascendum will provide the services and certified developers required to build AI-powered solutions and use cases that meet the customers' unique needs within these markets. People AI helps companies improve the performance of their sales teams by surfacing insights from sales behaviour and automating sales ramping and coaching. Microsoft Technology Licensing,, TalkDesk and Google are also in the running but need a significant amount of focus in the field to grow further.

Patent Filing Trend

The patent filing in the field of AI, especially in Sentiment Analysis, is rather bumpy. After the initial three years, a sharp spike can be seen where the number of filings rose from 146 to 348 within a year. Again the fifth year saw a slight dip, after which the trend saw exponential growth with the highest number of patent applications recorded in the eighth year (1030). Following years - ninth and tenth saw a fall in numbers. Sarcasm, negations, word ambiguity, and multipolarity are some issues faced while employing sentiment analysis. The recent advancements in AI, however, have fueled the industry, and it is ready to be back on track.

It is evident from the pie-chart that the US leads the domain with the highest number of patents to its name. It is the top country in Investment Monitor's first assessment of AI investor friendliness. The United States leads in eight of the 17 measures examined, including e-participation, emerging technology investment, and software spending as a percentage of GDP. Second, in line is China, with a recent boom in the AI technology sector. The private sector, university laboratories, and the military are working collaboratively in many aspects.

Difficulties of Sentiment Analysis

Due to the complexities of language, sentiment analysis must deal with at least a few challenges. It can be difficult to ascribe a sentiment classification to a sentence in some instances. That's where sentiment analysis based on natural language processing comes in handy, as the computer attempts to emulate natural human discourse.

  • Contrastive Conjunction - It is a challenge that a sentiment analysis system must deal with when one piece of writing (a sentence) contains two opposing words (both positive and negative). For example, "The weather was bad, but the hike was fantastic!"

  • Recognition of Named Entities - Another major issue that algorithms must deal with is named-entity recognition. Words have varied meanings depending on their context. For example, Is "Everest" a reference to the mountain or the film?

  • Anaphora Resolution - The problem of references inside a sentence, also known as pronoun resolution, specifies what a pronoun or a word refers to. For example, "We went to the theatre and then to dinner. It was a disaster."

  • Sarcasm - Is there a mechanism for detecting sarcasm in sentiment analysis? For example, "I'm so glad the plane is delayed. We doubt!"

  • The Internet - Lousy spelling, abbreviations, acronyms, lack of capitalization, and poor grammar affect the language economy and the Internet as a medium. It just so happens that any phrase used on the Internet takes on a life of its own. Sentiment analysis algorithms may have difficulty analyzing such pieces of writing.


Due to a huge number of real-world applications where uncovering people's opinions is vital in better decision-making, the discipline of sentiment analysis is an intriguing new study direction. People have recently begun to share their ideas on the Internet, which has increased the necessity for assessing opinionated online information for a variety of real-world applications.

Sentiment analysis is a rapidly developing field with a wide range of applications. Although sentiment analysis tasks are difficult due to their natural language processing origins, due to the huge demand for them, substantial progress has been made in recent years. Not only do businesses want to know how their products and services are viewed by customers (and how they compare to competitors), but customers also want to know what other people think before making a purchase.

For the foreseeable future, sentiment analysis and opinion mining will be significant due to the growing demand for product insights – and the technical obstacles that the sector is currently confronting. Opinion mining systems of the future will require a stronger link between extensive information bases and reasoning processes influenced by the human mind and psychology. This will lead to a better comprehension of natural language opinions and will help people communicate more effectively and bridge the gap between unstructured data in the form of human thinking and organized data that can be studied and handled by a machine more effectively.

As a result, intelligent opinion mining algorithms capable of managing semantic information, analogy, continuous learning, and emotion detection can be developed, resulting in very efficient sentiment analysis.



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