Sentiment Analysis or opinion mining is a machine learning and NLP (Natural Language Processing) technique. As the name suggests, it can analyze the emotional tone expressed by the author in any piece of text. Using sentiment analysis, businesses can analyze the sentiment value of their brands, products, or services.
One of the most wide-scale applications of sentiment analysis is analyzing customer feedback. A sentiment analysis program can analyze and evaluate the emotions/sentiments expressed by customers. Data analysts within large organizations use sentiment analysis to assess public opinions, monitor brand and product reputation, analyze customer experiences, and conduct market research.
Major brands and businesses often integrate third-party sentiment analysis APIs into their customer support and social media monitoring and management solutions. It helps them analyze the complaints, opinions, and suggestions of customers. Another popular application of sentiment analysis is the voice of employees’ solutions. It helps businesses analyze the opinions of employees with ease and identifies ways to increase productivity and efficiency.
How does Sentiment Analysis Work?
There are different algorithms that you can use to perform sentiment analysis. As for which algorithm you should use, depends on the size of the data you need to analyze and the accuracy that each algorithm provides. Let’s dive a little deeper and discuss the various sentiment analysis algorithms.
We can broadly classify the sentiment analysis methods into three categories,
Rule-Based Sentiment Analysis Algorithms
Rule-based sentiment analysis algorithms require the user to define rules. The system then classifies the unstructured textual data based on these pre-defined tools. Although, it also requires NLP techniques developed for computational linguistics, such as:
Stemming: Transforms inflected forms of words to their root form, thus making it easier for machines to understand and make sense of it.
Tokenization: It breaks down sentences into word tokens using blank spaces.
Part of Speech Tagging: It tags each word with the appropriate part of speech.
Parsing: It is classified into two types. Which type to use depends on the nature of the text you need to analyze and the level of complexity you want to attain.
- Dependency Parsing: It analyzes how two headwords are related and transformed by other words.
- Constituency Parsing: It analyzes the syntactic structure of sentences by identifying the phrase grammar structure.
Lexicons: A lexicon is the vocabulary of any language or subject.
How does it work?
- The user has to first create lists of words. One containing positive words and the other with negative words.
- The algorithm then analyzes the unstructured text to identify the pre-defined positive and negative words in any given sentence.
- Based on the number of positive and negative words, the algorithm classifies the text into positive and negative categories.
Automated Machine Learning-Based Algorithms
Unlike rule-based sentiment analysis algorithms, automated algorithms do not need a set of rules to classify sentiments. These algorithms use machine learning instead.
Most users usually take a supervised learning approach to build an effective sentiment analysis algorithm. The user has to first build labeled datasets to help the machine learn to classify text based on the emotional tone of the text. The user can then keep increasing adding more labeled data to further increase the accuracy of the analysis.
Automated sentiment analysis models may include the following types of algorithms:
- Support Vector Machines
- Linear Regression
- Naive Bayes
- Deep Learning
Hybrid Sentiment Analysis Algorithms
The hybrid sentiment analysis involves combining the desirable elements of both the rule-based learning and automated machine learning-based algorithms to classify unstructured text into positive, negative, and neutral categories.
Sentiment Analysis Challenges
Now that you know what sentiment analysis is along with the various algorithms to perform sentiment analysis, let’s discuss some of the major hurdles while performing sentiment analysis.
Anything that is said at some point in time, by someone, referring to someone, and so on. What I mean to say is anything said in context. Analyzing sentiment without context won’t help you find the exact sentiment expressed in any piece of text. But unlike humans, machines cannot detect or interpret context unless mentioned explicitly.
Irony and Sarcasm
When it comes to irony and sarcasm, people often use negative words to express positive sentiments. This can make it much more difficult for machines to classify such textual data.
— Lindsey Wasson (@lindseywasson) January 30, 2020
How would you classify the above tweet? It definitely is a positive comment appraising the talent of the artist. But the user uses some words that are often found in negative comments.
Negation is a means to reverse the actual meaning of words, phrases, or even sentences. Users apply various linguistic approaches to identify the source of negation, but it is just as important to analyze the range of words affected by the negation.
- “The movie was not interesting”. In this sentence, negation occurs before the words interesting and only affects one word.
- “I do not call this film an action movie”. In this sentence, the negation effects continue to the end of the sentence.
- “This will be his first and last masterpiece”. While the sentence does not include any negative words, it still carries a negative sentiment.
Word ambiguity is another hurdle you have to face while performing sentiment analysis. Here, it’s difficult to analyze the polarity of words as they strongly depend on the sentence context. A popular approach to overcome this hurdle is by creating lexicons. Although word polarity vastly differs in different domains, it’s impossible to develop a universal lexicon for sentiment analysis.
Applications of Sentiment Analysis
Let’s discuss some of the most popular applications of sentiment analysis.
Voice of Customers
The voice of customers is by far the most popular application of sentiment analysis. Brands and business organizations frequently use sentiment analysis to analyze customer feedback data such as:
- Customer reviews from forums
- Customer surveys
- Customer chats with support teams
Analyzing the customer feedback data can help identify recurring issues, identify patterns, and concerns. Thus, you can take effective measures to overcome these issues. Similarly, you can also identify customers with unhappy user experiences and take the necessary steps to retain those customers.
Voice of Employees
The voice of employees operates the same way as the voice of employees. The only difference is that instead of customers, it analyzes the feedback data of employees. Business enterprises often use the voice of employee solutions to analyze the opinions and reviews of employees.
Doing so helps them identify key issues that diminish the efficiency, productivity, and morale of the employees. By identifying key issues, businesses can take necessary measures to enhance overall productivity.
Social Media Monitoring
Businesses use social media platforms to promote themselves and find new customers. Some businesses even use multiple social media platforms to promote their brand. But if you have customers on social media platforms, you must track what they say about your brand, product, or services. This can help you identify what users like the most, as well as what needs to be improved.
Creating a perfect product or service is not an easy task. But you can surely improve it by analyzing the initial reactions of the consumers. You can use keywords to analyze the feedback on each feature of a product. Similarly, you can also keep evaluating the opinions of the target audience to decide on new features to add to the product or service.
Market and Competitor Research
No matter what industry you operate in, you can always learn from your competitors. Performing sentiment analysis on your competitor’s product or service can help you uncover insights to enhance the quality and functionality of your service and make it more appealing for the target audience.
You can also analyze the feedback data of competitors to identify unhappy customers. If your competitor does not bother retaining the said customer, you can use the opportunity to convert him/her into your prospective customer.
BytesView is an amazing machine learning and NLP-based paid text analysis tool that can help you analyze and extract actionable insights from unstructured text. BytesView also offers various text analysis models that can help you identify and extract relevant information from extensive volumes of textual data. The various analysis models are classified into two categories that are:
Image of text analysis models
Key features of BytesView:
- Dedicated plugins to integrate data
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- Compile and analyze large volumes unstructured text data
- Transform unstructured text into business intelligence
Sentiment analysis is one of the most effective tools that can help you better understand your customers. You can understand their needs and concerns of your customers and use the insights to mold products and services that live up to your customers’ expectations.
BytesView is an effective tool that can help you do that with ease. Analyze and extract game-changing insights that can help you scale your brand’s horizons.