Sentiment Analysis Applications and Examples

Sentiment Analysis Applications and Examples

Sentiment analysis applications – Traditionally, when an individual needs sentiment of people about any object such as product, event, person, etc. he or she ask friends and family members.

If any organization needs sentiments of the people they conduct surveys and opinion polls.

These traditional ways of acquiring sentiment data tend to produce very limited and structured information that can be managed manually without much effort.

Huge opinionated information about any entity or objects on the Web is too large to be managed manually. It creates a suitable ground for automated sentiment analysis tools.

Sentiment analysis tools are useful for manufacturers (or service providers) to assess consumer perceptions about their services and product. At the same time, these are beneficial for the consumer to gain insights about the product and service also.

 

Sentiment Analysis Example

There are various sentiment analysis examples. Some of the examples are as below-

• You can find out the mood of the public about the present or past government by analyzing the textual data present on the internet and social networking sites about the government. It can be tweets, chats, messages, blogs, etc.
• You can find out the subjective opinion of the public about an event like a surgical strike, Pulvama event, etc. via sentiment analysis tools and techniques.
• You can find out the sentiments of the public about a movie through the reviews that are present on the internet and social networking sites about the movie.
• You can get public mood about any burning problem of the country such as population growth, pollution, COVID-19, demonetization, third world war, etc.
• You can get public opinion about the present education system.
• If you want to do online shopping and want to know about the best website for shopping you can analyze reviews present on the internet and review sites.
• You can analyze public sentiments about any product which you want to purchase from the market whether online or offline.
• If a businessman wants to improve his business he can analyze reviews present on the internet about their brand or product.
• Which insurance policy is good and which is not good for your future you can analyze it through the feedback of the people present on the internet about the insurance policies.
• Hence, the list is big. Through sentiment analysis, you can get the public mood, public opinion about anything.
• Sentiment analysis is useful for both consumers and producers.

 

There is no field where you can’t use these sentiment analysis tools. It can be used in all those areas where you want to find the opinion of the public.

It has huge applications. Some of the important sentiment analysis applications are as follows-

 

List of Sentiment analysis applications

 

 

• Sentiment Analysis applications to know the Popularity of an event or activity
• Sentiment Analysis to know Same wavelength communities
• Sentiment Analysis in Tourism Industry
• Sentiment Analysis for Presidential Elections
• Sentiment Analysis for Insurance Policy Research
• Reviews for Product Marketing
• Sentiment Analysis for Segmenting people about a certain issue
• Sentiment Analysis for Future forecasting of fashion
• Sentiment Analysis of Website Reviews to improve customer satisfaction
• Sentiment Analysis on Movies Reviews
• Sentiment Analysis of public about present government through online social media data
• Sentiment Analysis for career selection
• Sentiment Analysis to find the impact of a movie on juvenile delinquency
• Sentiment Analysis to reduce employee turnover
• Sentiment Analysis of Multilingual data of any domain of interest
• Sentiment Analysis to know the impact of movies on present society
• Sentiment analysis stock market

 

• Sentiment Analysis applications to know the Popularity of an event or activity

To know the popularity of an event, program, or activity you need the opinion of the public.

For example, if you want to organize some program in your area which was conducted in past days also, you can collect the opinion of the public about the past program area to make proper planning of the program you are going to organize in the future.

You can collect opinions of the public from their online data also such as blogs, forums, websites, tweets, comments, etc. to know the popularity of an event.

 

• Sentiment Analysis applications to know Same wavelength communities

You can find out similar communities or groups about any domain through the opinion mining of the public.

 

• Sentiment Analysis applications in Tourism Industry

This is also a growing industry today. Before doing planning to go on a trip you do huge research to know the best place to visit.

People write their reviews and publish online. You can collect all those reviews and can do proper planning before going on your trip.

 

• Sentiment Analysis applications for Presidential Elections

For the presidential election, various companies collect the opinion of the public to know which party is going to win.

People give their views in the form of comments, images, tweets, and feedback before the presidential elections. You can collect all those from social media sites and can forecast the results.

 

• Sentiment Analysis  applications for Insurance Policy Research

Insurance policy research can be done on both the ends from the insurance company’s point of view and the customer point of view.

Insurance companies can launch better policies for the public by knowing public opinion or sentiments about their policies.

People also can get better policies after getting public opinions about the policies.

 

• Reviews for Product Marketing

The company can collect reviews about their product to improve the quality of products and also to compete with the competitors.

A company can better plan their marketing strategies to improve the sale after getting a public opinion about their product.

 

• Reviews on Online education

Due to advancements in technology and internet online education is going to common more and more.

Before getting enrolled in the educational program you can get the opinion of the students who have already passed out some online courses.

Institute and universities who are providing online courses can also collect public reviews and feedbacks to better design online courses.

 

• Sentiment Analysis applications for Segmenting people about a certain issue

Opinion mining is used to segment the people about any issues for instance political issues, religious issues, issues related to crimes, education, entertainment, etc.

 

• Sentiment Analysis applications for Future forecasting of fashion

Nowadays, movies play an important role in the fashion industry. People copy style and fashion from popular movies.

Through the data present in the social media about the movies and fashion companies can forecast the coming style and fashion.

 

• Sentiment Analysis of Website Reviews to improve customer satisfaction

The website can be improved and better designed after collecting reviews about the website. If it is an e-Commerce website then people can also extract reviews and feedback before doing any purchase from the website to know which website is better and reliable for online shopping.

 

• Sentiment Analysis applications on Movies Reviews

The public see the movies and give their feedback about the movies through tweets, comments, blogs, and forums. Before plan to view the movie, you can get public opinion and then plan to watch the movie.

 

• Sentiment Analysis of public about present government through online social media data

You can collect the views of the public about the present government working style. You can judge the mood of the public through their mood.

 

• Sentiment Analysis applications to reduce employee turnover

If employee turnover is greater in a certain organization company can collect the feedback of the employees and understand the cause of the turnover and can design future strategies to reduce employee turnover.

 

• Sentiment Analysis applications of Multilingual data of any domain of interest

Opinion mining can be done on multilingual data too about any domain of interest such you can collect online tweets and comments which are published in Hindi or any other language to know the opinion of the public of any other language also.

 

• Sentiment Analysis to improve education system online and offline both

Online and offline education can be improved through opinion mining. Some educational modules are more careers oriented and some not. After extracting the opinion of the students’ better course can be designed.

 

• Sentiment Analysis of market data for future investments decisions

If you want to do some investment for your future you can mine the public opinion about various policies and programs and can take better decisions.

 

• Sentiment Analysis for career selection

Before making decisions about your future and career you can collect the reviews of the public available on the online repositories and better decisions you can make.

 

• Sentiment Analysis to find the impact of the movie on juvenile delinquency

Movies have a great role in destroying society and making society. How much youth is influenced by the movies can be accessed from the reviews of movies present online.

 

• Sentiment Analysis applications to know the impact of movies on present society

Moreover, the impact of movies on the public can be judged through tweets, reviews, and comments published by the people about the movies. Sometimes criminals and antisocial people learn so many ideas from the movies and apply them in real life.

Therefore there are various Opinion Mining examples. Where we need decision making based on the public mood and public sentiments we require opinion mining in the related domain.

 

• Sentiment analysis stock market

The Internet has removed the barriers of brokers and geographical locations because now investors can buy and sell shares after investigating the stock market status from anywhere at any time. Before investing money, it is important for investors to forecast the stock market using tools such as sentiment analysis and opinion mining.

 

How does sentiment analysis work

 

 

Sentiment analysis work at three levels as given below-

• Document Level,
• Sentence Level and
• Aspect level.

 

Document Sentiment Analysis

Document-level sentiment analysis is done on the whole corpus of sentences, tweets, comments, reviews, etc, and categorizes as Positive, Negative, or Neutral. The overall document is assessed whether the document is Positive or Negative.

 

Sentence level Sentiment Analysis

Sentence level classification considers comments, statements, or tweets as a sentence and calculates the sentiment related to the sentence.

Aspect Level Sentiment Analysis

The aspect/feature level provides a more fine-grained model in which sentiments or opinions can be extracted from different aspects or features of the entity. In this level, opinions or sentiments are extracted and assigned them a related class by determining the polarity to conclude the result.

Aspect-based sentiment analysis is a text analysis technique that breaks down the text into aspects (attributes or components of a product or service), and then allocates each one a sentiment level (positive, negative, or neutral).

 

Approaches of Sentiment Analysis

Existing work on sentiment analysis based on mainly two types of approaches:

• Traditional approaches and semantic approaches. In traditional approaches presence of words or syntactical features are used for sentiment analysis. This approach relies on the existence of words that reflect the sentiment explicitly.
• On the other side, Semantic approaches, exploit the latent semantics of words in the text to find out their context and update their sentiment dimensions accordingly.

Sentiment analysis can be defined as the task categorizing or recognizing the text as positive, negative, or neutral.

It is a multidimensional task, which exploits various techniques from

• Natural Language Processing and
• Machine Learning,

to perform various detection tasks at different text-granularity levels.

 

• The sentiment analysis task which varies from polarity detection to more extracted tasks, such as emotion detection and sentiment strength detection.
• The sentiment analysis level, which is determined based on the granularity of text used for the analysis (e.g. word-level, document-level, sentence-level, etc.).
• The sentiment analysis approaches used (e.g., supervised approaches, lexicon-based approaches, hybrid approaches).
• The data type used: sentiment can be extracted from microblogging data (tweet messages, Facebook status updates, SMS, etc.) as well as conventional text (e.g., news articles, product reviews).

 

Example: How sentiment analysis work

The following review about the iPhone7 as an example to illustrate the different dimensions of sentiment analysis

(1) I have recently upgraded to iPhone7. (2) I am not happy with the screen size. (3) It is just too small :(. (4) My best friend got Galaxy S7. (5) He got a much larger screen than mine!!!. (6) Even if his hardware outperforms mine, I easily outrun him with all the apps I can get 🙂

The detailed analysis of this example is as below-

• Polarity Detection

The basic subtask in sentiment analysis is polarity detection, that is, decide whether the sentiment of a given text is positive or negative. In the above example, sentences (2) and (3) have negative sentiment while sentence (6) has a positive sentiment.

• Subjective Detection

Another popular subtask is subjectivity detection, which aims to identify whether the text is subjective (i.e., has a positive or negative sentiment) or objective, (i.e., has a neutral sentiment). For example, sentences (1) and (4) are objective sentences, i.e., they are factual or neutral. On the other hand, sentences (2), (3), (5), and (6) are all subjective.

Subjectivity and polarity detection is the most popular subtasks in sentiment analysis but not the only ones.

Other relevant subtasks include:

• Emotion Detection

(i) emotion detection, which aims to identify the human emotions and feelings expressed in the text, such as “happiness”, “joy”, “anger”, “sadness”, etc. In the above example, the sentence (3) shows “sadness” emotions while sentence (6) shows “happiness” emotions.

• Sentiment Strength Detection

(ii) Sentiment strength detection, which aims at measuring the strength or the intensity of the sentiment in text. For example, this task tries to detect how strong the positive sentiment is in the sentence (6) and how strong the negative sentiment is in the sentence (3).

All the above-mentioned tasks have been extensively researched in the literature, aiming at analyzing the sentiment at four different text granularity levels.

 

The granularity of the Analysed Text

Each one of these levels differs from the others in the level of granularity of the analyzed text, as follows:

Word-level sentiment analysis: If a word w is given in a sentence s, It is needed to decide whether this word is opinionated (i.e., express sentiment) or not. If so, detect what sentiment the word has.

When the word is categorized as a named entity (i.e., the word represents an instance of a predefined category, such as Person, Organisation, Place, Product, etc.), the task is called entity-level sentiment detection.

In the example, the entity “iPhone” receives a negative sentiment on average while the entity “Galaxy S7” receives a positive one.

• Phrase-level sentiment analysis

This is also known as expression-level sentiment analysis. Given a multi-word expression e in a sentence s, the task is to detect the sentiment orientation of e. For example, the expression “I am not happy” in a sentence (2) has a negative sentiment.

• Sentence-level sentiment analysis

In a given a sentence s of multiple words and phrases, decide on the sentiment orientation of s. For example, sentence (2) has a negative sentiment while sentence (6) has a positive sentiment.

• Document-level sentiment analysis

In a given document d, decides the document sentiment by analyzing the whole document d. This is usually done by averaging the sentiment orientation of all sentences in d.

In a text, when a sentence describes a specific feature or aspect of a common object (e.g., product), the sentiment analysis task is called aspect-level sentiment analysis.

• Aspect Level Sentiment Analysis

Aspect-level sentiment analysis is defined as given a document d, an object o, and a set of aspects A, detect the sentiment expressed in d towards each aspect a_i € A of the object o.

For example, in the sentence (2) the aspect “screen size” of “iPhone” has a negative sentiment while the same aspect of “Galaxy S7” has a positive sentiment.