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

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

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 of how customers are thinking about your brand?

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

This is the most common query that arises in the minds of the 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 businesses can find the performance of their marketing campaign or regarding their products

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

What does it mean?

Sentiment Analysis:

It can also be named as data mining where the technologies like Natural Language Processing (NLP), text analysis and computational linguistics can be used to identify and extract the subjective web information especially from social media, tweets, and posts, etc. Where the varied applications from marketing to customers service takes place then 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 the sentiment analysis mainly comes under social media monitoring. In these days, the 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 the companies might have the bundles of customer feedback, a human can’t analyze such data manually. In such cases, sentiment analysis provides which are the most significant issues from the customer point of view. Here the automation of it can be made basing 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 the details of text on different levels that entirely depends on our goal. That means from the group of reviews if you want to measure what percentage of customers are enjoying your products and which are not. Are they comparing your brand products with others in the market? To find all those, you should focus on emerging specific keywords along with some other aspects.

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 in analyzing the sentences than whole document analysis. Perhaps it can be used in both cases. For the 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 a subjective sentence, the customers express their attitude towards a particular subject, just like about the company. In the form of an actual sentence, 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, then you need to find that emotion belongs to a positive, negative, or neutral state. In some cases, people share their opinion without emotions.

Fine-grained:

If you need to get accurate results of sentiment analysis, then you can choose this type. It identifies the target of sentiment that means which is more concentrated in the discussion. By breaking the sentence into phrases, you can analyze each phrase by connecting with competitors. What the person is precisely 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 feedbacks.

Lexicon-based analysis:

Refer to the polarity score in this method uses different words in deciding the general assessment score of the content. Here the weak point of this analysis is the massive words and expressions are not included in the sentiment lexicons, and the strong point is that it doesn’t require training data.

Individual Target:

The analysis looks at individual mentions or aggregates for sources or trends. When you are tracking as the customer service, then you need to consider each mention.

Integrated analysis:

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

NLP automation:

In this, the automation analysis of unstructured sources like images, text, video, and text. Analysis by trained humans and crowd-sourced analysis by untrained humans can 1be done. Analyzing survey questions by applying NLP automation with linguistic, machine learning, and statistical technologies.

Hybrid:

With the combination of the lexicon-based and machine learning approach, the sentiment analysis will be addressed, which is Hybrid sentiment analysis. It provides more precise results than other approaches, but it 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, or bar graphs, etc. While dealing with these, you might not understand how to make business decisions by using that information.

Advantages of Sentiment Analysis

Sentiment analysis is a powerful tool when it is used perfectly. By applying sentiment analysis, the 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 the values between 0 and 1. Here 0 is a negative sentiment, and 1 is a positive sentiment. The users can use these in-built visualizations and make customization. The data visualization conveys the information to decision-makers, which lets them understand the vital information easily. The generated reports are real-time valuable and interactive to connect sentiment analysis with social media in collecting new insights.

Data Storytelling:

Sentiment analysis the users to create new data storytelling that 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 the requirements like processes, embedded analytics, and workflows of many corporations. Actionable analytics accelerating the decision-making process of data-driven companies.

Don’t Require Data Scientist:

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

Streaming Data Sets:

The users can make use of variable data sets from different sources like the web, SQL server, text, etc. Most of the tools are offering the service of unlimited connectivity as the data coming from variable data sources. Most of the companies are moving their data to the cloud.

Basics of Sentiment Analysis

It is somewhat difficult for brands to analyze massive reviews of public or customer opinions on social media. Simultaneously you can launch sentiment analysis in a complex and simple way. But the Natural Language Processing technology of AI letting the robots or machines to understand and speak the 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 can start scoring each page or paragraph on the website.

The use of 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:

Mostly in text classification problems, the naïve Bayes technique is applied, and it aims to assign documents like tweets, news, emails, and posts, etc. The objective of Naïve Bayes in Sentiment Analysis is to define the writer’s point of view regarding a particular topic, brand and products or services, etc.

Neural Networks:

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

Reviews and Labels:

Mostly data contains 25000 IMDB reviews, and each review is stored in the file reviews.txt in a single line. The pre-processed reviews contain the 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 done by building the metric like counting the words. In positive reviews, a word with sentiment ratio of 1 is used, 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 in the same way words like horrible and bad, etc. in negative reviews. Through this word counting, you can find a list of words that are most commonly used by the audience and which are appearing most frequently in positive and negative reviews.

Sentiment Analysis Use Cases

Customer Targeting:

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

Pointing the disappointed customers:

Sentiment analysis helps in identifying customers who are having a negative impact on your products or services. Which lets you address their worries. Moreover, you can build close customer relations by concerning their issues, and that prolonged activity boosts positive public response, particularly about your brand.

Identifying key promoters and critics:

Some audience may have a favorable opinion about you, and they love to give more positive reviews which come under promoters. In the same way, we can find more critics who are mostly being on social media, and if they don’t see the best in your products or services, they immediately give feedback. They play a crucial role 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 pick find which are mostly influencing 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:

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

How Brands use Sentiment Analysis?

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

Decision Making:

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

Company Monitoring:

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

Optimizing Campaigns:

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

Product Monitoring:

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

Product Development:

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

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 huge amount. 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 negative data that is irrelevant to your products from reviews. Data, objective, and subjective segmentation are possible. The sentiment extraction through sentiment lexicon-based method. To do all these, the following algorithms are used.

  • 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 tough task for industries and researches. We know that sentiment analysis categorizes the text as positive, negative, or neutral. Hence, it can be considered as the text classification task. Here is the list of challenges that all businesses should face while dealing with sentiment analysis.

Multi-method research plan:

The sentiment analysis data from social media sometimes couldn’t explain why that particular event occurred and from which demographic group is obtained. So it needs to conduct sentiment analysis along with a survey to find the right comments.

Sarcasm Detection:

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

Use machine learning and human knowledge:

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

Negation Detection:

The division of words, sentences, and phrases is reversing way in the negation of linguistics. To find whether negation occurrence the researches different rules of linguistics. Simultaneously, it necessary to define the range of words that are going under negation words.

Comparisons:

It is the most challenging task for the business teams while making 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.

Emojis:

The sentiment analysis over tweets needs special attention to both the word level and character level. Without considering how much attention you pay, it requires a lot of pre-processing.

Defining neutral:

To perform accurate sentiment analysis, the neutral review is a great challenge. Understanding the neutral reviews from the list of positive or negative comments will take much time.

Noise Removal:

Social media data consists of a lot of noise like irrelevant comments; a bot created content and advertisements, etc.

How to do sentiment analysis for Personal Brand?

92% of marketing professionals are stating that social media has a profound impact on their business growth. Which projects that a brand should play a highly competitive game on social media to attain the potential customer’s attention. Sentiment analysis is the most prominent tool for brands to understand the customer’s behavior.

Handle customer complaints:

Through the medium of social media, you can solve the issues of customers 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. To make it more effective, you can assign the representative to handle those queries.

Differentiating from the crowd:

When you consider your client’s feedback, then 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 in a similar way for the negative reviews so that there will more chances to acquire the audience’s attention.

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. The preventive measures for negative comments should be taken. All these can help business reputation from further getting spoiled.

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