Sentiment Analysis: Definition,Types, Tools, and Use Cases

Digital Advertising
Sentiment Analysis

The success or failure of any business brand hinges on customer opinion. Suppose your product or service was released in the market.

Have you ever thought about what customers think about your brand?

Do they like your products, services, ad campaigns, and company?

This is the most common query that arises in the minds of business brands. This is why you need sentiment analysis on social media platforms. Sentiment analysis will let the brands know how social media users feel about a particular product or service. By fetching analytics through sentiment analysis, any business can find the performance of their marketing campaign or their derivatives.

Sentiment analysis is most significant in figuring out the framework of online mentions. Measuring the audience’s opinion about the products or events through data analysis can let your team find which marketing strategy can work better to drive sales and improve ROI.

What does it mean?

Sentiment Analysis:

It can also be named data mining, where the technologies like Natural Language Processing (NLP), text analysis, and computational linguistics can be used to identify and extract personal web information, especially from social media, tweets, posts, etc. Where the varied applications from marketing to customer service take place, it is applied to reviews and social networks. It is merely the computational activity of obtaining sentiments, the subjectivity of a text, and opinions.

In marketing, timing analysis mainly involves social media monitoring. These days, audiences are more active on social media channels in the quick formation and spreading of positive, negative, or neutral talk of any brands in the market. Though companies might have bundles of customer feedback, humans can’t analyze such data manually. In such cases, sentiment analysis provides the most significant issues from the customer’s point of view. Here, the automation can be made based on the collected data rather than null perceptions.
The set of insights through sentiment analysis can be used to make effective business strategies, objectives, and decisions.

Types of Sentiment Analysis

We can analyze text details on different levels that entirely depend on our goal. That means from the group of reviews, you can measure what percentage of customers are enjoying your products and which are not. Are they comparing your brand products with those of others in the market? It would be best to focus on emerging specific keywords and other aspects to find all those.

Coarse gained:

This analysis type is done on document and sentence levels. Most specialists use it to analyze sentences rather than whole documents. Coarse-grained SA entails two coherent tasks: subjectivity classification and sentiment detection and classification.
This type of sentiment analysis is used to analyze the sentences rather than the complete document analysis. Perhaps it can be used in both cases. For successful coarse-grained sentiment analysis, two intelligible entities are required. Such is

  • Subjective or objective classification:

Here we analyze whether the sentence is disclosing subjective or objective. In an emotional sentence, the customers express their attitude towards a particular subject, just like about the company. In actual punishment, they deliver facts about the topic or objects.

  • Sentiment detection and sorting:

This analysis aims to identify whether the sentence contains the sentiment or not. If it is there, you need to find that emotion belongs to a positive, negative, or neutral state. In some cases, people share their opinion without feelings.


You can choose this type to get accurate sentiment analysis results. It identifies the target of sentiment, which is more concentrated in the discussion. You can analyze each word by connecting with competitors by breaking the sentence into phrases. What the person is talking about the product and who talks can be identified through this. Also, you can discover why the consumer gauges in that particular way. Moreover, you can use it to find comparative feedback.

Lexicon-based analysis:

Ring refers to the polarity score; this method uses different words to decide the content’s general assessment score. The weak point of this analysis is that the massive words and expressions are not included in the sentiment lexicons, and the vital point is that it doesn’t require training data.

Individual Target:

The analysis looks at individual mentions or aggregates for sources or trends. When tracking customer service, you need to consider each word.

Integrated analysis:

The integrated analysis requires the link between sentiment and customer behavior, demographics, events, emotional profiles, transactions, etc.

NLP automation:

The automation analyzes unstructured sources like images, text, video, and text. Analysis by trained humans and crowd-sourced analysis by untrained humans can 1be done. I am exploring survey questions by applying NLP automation with linguistic, machine learning, and statistical technologies.


With the combination of the lexicon-based and machine learning approach, the sentiment analysis will be addressed: Hybrid sentiment analysis. It provides more precise results than other approaches but is less commonly used.

ROI analysis:

The implementation of sentiment analysis projects the business value. Many social media analytics tools and business intelligence dashboards will display the sentiment through trend lines, pie charts, bar graphs, etc. While dealing with these, you might not understand how to make business decisions using that information.

Advantages of Sentiment Analysis

Sentiment analysis is a powerful tool when it is used ideally. By applying sentiment analysis, brands can understand and analyze customer behavior. Depending on that insights, the companies take action to improve the growth of the business. Here are some more benefits that are being added by sentiment analysis. Let’s have a look over them.

Data Visualization:

After collecting the data, it is necessary to convert to a team to act according to it. The sentiment analysis score generated by text analytics tools uses values between 0 and 1. Here, 0 is a negative sentiment, and 1 is a positive sentiment. Users can use these built-in visualizations and make customizations. The data visualization conveys the information to decision-makers, which lets them understand the vital information easily. The generated reports are valuable and interactive in real-time to connect sentiment analysis with social media in collecting new insights.

Data Storytelling:

Sentiment analyzes the users to create new data storytelling companies are looking for. We can turn the critical data into actionable data where the sentiment analysis humanizes and visualizes the collected data. These provide powerful strategies to fulfill many corporations’ requirements, like processes, embedded analytics, and workflows. Actionable analytics accelerate the decision-making process of data-driven companies.

Don’t Require Data Scientist:

You can obtain 1000 scores, including positive, negative, and neutral ratings, with one API offered by the sentiment analysis tools. The Text Analytics service will provide Natural Language Processing. Givit’s unstructured text will analyze sentiment, identify the most-known entities, and extract key phrases. Through these features, companies can quickly find what

Streaming Data Sets:

Users can use variable data sets from different sources, such as the web, SQL server, text, etc. Most tools offer unlimited connectivity as the data comes from variable data sources. Most companies are moving their data to the cloud.

Basics of Sentiment Analysis

It is somewhat tricky for brands to analyze massive reviews of public or customer opinions on social media. Simultaneously, you can launch sentiment analysis in a complex and straightforward way. However, the Natural Language Processing technology of AI allows robots or machines to understand and speak human language.

In that case, IBM Watson is the most commercial and popular product in the present market. Also, we can collect the text from webpages automatically and start scoring each page or paragraph on the website.

Python, Beautiful Soup, and requests can be used for web scraping, which makes your work simple. Here is the list of other factors involved in Sentiment Analysis.

Naïve Bayes of Sentiment Analysis:

In text classification problems, the naïve Bayes technique is applied, aiming to assign documents like tweets, news, emails, posts, etc. The objective of Naïve Bayes in Sentiment Analysis is to define the writer’swriter’sf view regarding a particular topic, Brand products or services, etc.

Neural Networks:

Neural networks can work better in sentiment analysis. You need to transform the collected data into a format through which the neural networks can understand better. To do this, you should convert the customer reviews into numerical vectors.

Reviews and Labels:

Mainly data contains 25000 IMDB reviews, and each review is stored in the file reviews.txt in a single line. The pre-processed reviews have lower-case letters. Labels.txt file consists of the matching labels. Then each review is marked as either positive or negative.

Sentiment Ratio:

The sentiment ratio can be calculated by building the metric, such as counting the words. A term with a sentiment ratio of 1 is used in positive reviews, and -1 is used in negative reviews only.

Words Counting:

We can find the most common words, like Awesome, Amazing, and superb, etc., in positive reviews and, precisely, words like horrible and evil, etc., in negative thoughts. Through this word counting, you can find a list of terms most commonly used by the audience and which appear most frequently in positive and negative reviews.

Sentiment Analysis Use Cases

Customer Targeting:

Brands can easily find customers’ opinions about their products and brands with the help of sentiment analysis. Depending on the sentiment score, you can make customer segments and provide different offers to each group.

Pointing the disappointed customers:

Sentiment analysis helps identify customers hurting your products or services, which lets you address their worries. Moreover, you can build close customer relations by addressing their issues, and prolonged activity boosts positive public response, particularly about your brand.

Identifying key promoters and critics:

Some audiences may have a favorable opinion of you and love to give more positive reviews, which come under promoters. In the same way, we can find more critics who are primarily on social media, and if they don’t best your products or services, they immediately give feedback. They are crucial in affecting your Net Promoter Score (NPS). The emulsion of data science can explain it. By comparing the reviews of critics and promoters, you can easily find which mainly influences the NPS score.

“Net Promoter Score (NPS): It is the measuring index from -100 to 100, which can be used to gauge the customer’s willingness in recommending the particular brand’s products or services to the others.”

Brand Monitoring:

You can maintain a long-lasting brand reputation by retrieving and monitoring the data from different sources like customer emails, social media postings, product reviews, etc., Also, you can easily monitor the sentiment score by using sentiment analysis.

How do Brands use Sentiment Analysis?

The sentiment analysis strategy fuels business analysis by enabling innovative ideology in the market.

Decision Making:

Many types of research show that social media opinions and news are greatly influencing brand sales. Online content’s subjective and informational entities can affect the stock price, market activity, and trading volume. Businesses can implement the marketing strategy by incorporating sentiment data into decision-making.

Company Monitoring:

The company can take measures to protect its brand reputation by scrapping the sentiment analysis data. Also, business intelligence teams can take advantage of positive publicity and lessen negative sentiments.

Optimizing Campaigns:

Political opinions are the most emotionally capable views that people hold. The sentiment analysis shows more impact on politics as it provides information on voting behavior, opinion changes, campaign success, etc. It is necessary to pay attention to sentiment analysis to find information about candidates, presidential job approval, legislative bills, and campaigns.

Product Monitoring:

If there is no understanding of sentiment analysis, then the Business Intelligence strategy will be incomplete. The products or services portrayed in social media reviews or news articles will have more influence from the bottom line. By sentiment analysis of your product data, businesses can assimilate that data into AI-driven business solutions and produce actionable insights into which products or services are performing better or which are not.

Product Development:

A negative review will cost a million dollars in company sales. The teams with data-driven insights use web-scrapped sentiment analysis data to find what changes customers need and the product quality performance. Sentiment analysis is vital in product planning, creation, and development.

Crisis Management:

The brands can lessen the damage caused due to negative communication by real-time monitoring conversations between discussers. Most of them could not handle the social media calamities for what they pay in massive amounts. Through sentiment analysis, businesses can manage these crises and engage new loyal customers.

How Is Sentiment Analysis done?

Sentiment analysis is done to remove noise, such as cleaning the harmful data that is irrelevant to your products from reviews. Data, objective, and subjective segmentation are possible. The sentiment extraction through sentiment lexicon-based method. The following algorithms are used to do all these.

  • Naïve Bayes
  • SVC
  • BernoulliNB
  • GradientBoostingClassifier
  • GaussianNB
  • LinearSVC
  • DecisionTreeclassifier
  • ExtraTreesClassifier
  • LogisticRegression
  • RandomForestClassifier
  • MultinomialNB
  • KNeighborsClassifier
  • SVC
  • NuSVC
  • Maximum Entropy Model

Sentiment analysis challenges

Sentiment analysis is a challenging task for industries and researchers. We know sentiment analysis categorizes the text as positive, negative, or neutral. Hence, it can be considered the text classification task. Here is the list of challenges businesses should face while dealing with sentiment analysis.

Multi-method research plan:

The sentiment analysis data from social media sometimes tell why that particular event occurred and from which demographic group is obtained. So it needs to conduct sentiment analysis and a survey to find the correct comments.

Sarcasm Detection:

The audience mentions negative sentiments using positive words in this type of text. Through this, businesses can easily cheat using sentiment analysis models unless they take into account the possibility of it occurring. The user-generated content, like Tweets, Facebook content, etc., has more chances to get sarcasm. Without a good knowledge of the context of a specific topic or situation, it is difficult for brands to launch sarcasm detection in sentiment analysis.

Use machine learning and human knowledge:

It is difficult for humans to implement the prior knowledge they achieved from their experience. Because machine learning can be isolated, and humans don’t need isolation.

Negation Detection:

The division of words, sentences, and phrases is reversed in the negation of linguistics. To find whether nullification occurs, the researchers use different rules of linguistics. Simultaneously, it is necessary to define the range of words that are going under negation words.


It is the most challenging task for the business teams while doing sentiment analysis. The text format like ‘this is better than that; this is better than nothing, etc. These types of phrases are somewhat difficult to classify.


The sentiment analysis of tweets needs special attention on both the word and character levels. It requires a lot of pre-processing without considering how much attention you pay.

Defining neutral:

An unbiased review is a great challenge when performing accurate sentiment analysis. Understanding unbiased reviews from the list of positive or negative comments will take time.

Noise Removal:

Social media data consists of much noise, like irrelevant comments, bot-created content, advertisements, etc.

How to do sentiment analysis for a Personal Brand?

92% of marketing professionals state that social media profoundly impacts their business growth, which projects that a brand should play a highly competitive game on social media to attain potential customers. Sentiment analysis is the most prominent tool for brands to understand the customer.

Handle customer complaints:

Through the medium of social media, you can solve customers’ issues in real-time. How much speed you respond to the negative comments of the audience on social media will be the most significant factor in gaining a reputation. You can assign a representative to handle those queries to make them more effective.

Differentiating from the crowd:

When you consider your client’sclient’sk, you will be their ‘go-to’ ‘business. Monitor social media sentiment to find what your customers are talking about you. That means if they mentioned you by appreciating your service or products, then make a reply by thanking them. Treat the negative reviews in a similar way so that there will be more chances to acquire the audience’s opinion.

Building brand image:

Analysis of the response rate of the audience after launching the new products or any other events helps you make an effective strategy. In the part of the marketing strategy, positive sentiments should be developed and implemented. Preventive measures for negative comments should be taken. All these can help prevent the business reputation from further getting spoiled.


Sentiment analysis has emerged as a powerful tool for businesses, enabling them to extract valuable insights from unstructured data and gain a deeper understanding of their customers’ emotions, opinions, and experiences.
By leveraging advanced natural language processing techniques and machine learning algorithms, sentiment analysis helps organizations make informed decisions, improve customer experiences, and optimize their strategies for success.
As sentiment analysis techniques continue to evolve, staying up-to-date with the latest advancements and best practices is crucial for businesses looking to harness the full potential of this powerful technology. By investing in sentiment analysis, organizations can unlock the wealth of information hidden within their data and drive growth by staying attuned to their customer’s needs and preferences.

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