Sentiment analysis is the procedure of determining if the piece of writing is negative, positive or neutral. A system for sentiment analysis in the text analysis process combines techniques for machine learning along with natural language processing (NLP) to allocate the subjective sentiment scores to the topics, themes, categories and entities within a single phrase or a sentence.
Sentiment analysis is used by the data analysts of large organizations to estimate the opinions of the public while conducting market research, the reputation of the products, brand monitoring, and understanding of the customer experiences. The companies that deal with data analysis integrate third party APIs for sentiment analysis to offer useful insights to the customers through proper management of customer experience, monitoring of social media, or platform for analytics workforce.
Types of Sentiment Analysis
The different types of sentiment analysis are as follows.
- Standard Sentiment Analysis: The standard form of sentiment analysis identifies the nuances in the opinions as negative, positive, or neutral. It is the most popular one among the various kinds.
- Emotion Detection Sentiment Analysis: Sentiment analysis for emotion detection identifies the feeling that is hidden in a text. It acts as an association between emotions and phrases like happiness, frustration, anger, etc.
- Intent Detection Sentiment Analysis: Intent Detection sentiment analysis identifies the action for a given opinion. Classification of the intent of the user allows discovering priceless information on a service or product. Sentiment analysis helps to easily spot the issues related to customer experiences.
- Fine-grained Sentiment Analysis: Fine-grained sentiment analysis adds extra categories to achieve more granular results. The categories can be classified as:
- Very Positive
- Negative and
- Very Negative
- Aspect Based Sentiment Analysis: Aspect Based Sentiment Analysis focuses on the features and aspects that are present in an opinion. Product reviews consist of various opinions about different traits of a service or product.
Working Procedure of Sentiment Analysis
The sentiment analysis of text documents follows a simple process as below.
- Breaking down each text document into the component parts like phrases, sentences, parts of speech and tokens.
- Identification of each phrase and component bearing a sentiment
- Assignment of the sentiment score in each of the components and phrases (from -1 to +1)
- Combining scores for the sentiment analysis in multilayer
Sentiment libraries are a huge compilation of adjectives like wonderful, good, awful, and horrible, and phrases like a wonderful story, awful performance, horrible show and a good game that has been scored by the hand of the human coders. In other words, a sentiment analysis structure illustrates a sentiment library to recognize the phrases bearing a sentiment it comes across during the lifetime. This manual scoring process of sentiment is a very tricky process because everyone who is involved in this process needs to reach a common agreement on how weak or strong the score is when compared to the other scores.
For instance, if one person gives -1 sentiment score to the word bad, and another member gives -1 to the word awful, then the sentiment analysis system will show results that both the words are evenly negative. Sometimes there is a multilingual sentiment analysis system. For this type of system, there should be separate libraries to support each of the languages. Each of these libraries should be maintained and updated regularly by tweaking the scores, adding new phrases, and removal of irrelevant phrases.
After the preparation of the sentiment libraries, coders implement a series of guidelines or rules that will assist the computer to evaluate and express the sentiments toward a particular entity. It could be a pronoun or noun based that is near to the negative and positive adverbs and adjectives. Whenever a query returns a hit count, it represents the total number of times the word has appeared near an adjective or adverb. The system then combines the hit counts using a mathematical procedure called log odds ratio. The outcomes are a sentiment score usually on the scale of -1 to +1 for each expression.
Applications of Sentiment Analysis
Sentiment analysis is the most efficient tool that is used for the Voice of the Customer and the Employee. Sentiment analysis is employed to recognize how employees and customers think about a particular subject and why they feel in this way. Sentiment analysis is chiefly implemented by product managers, human resources, Business analysts, directors of customer support, and other stakeholders.
Voice of customer’s sentiment analysis
In this age of social media, a negative viral review can bring down even an entire big band. The automation of sentiment analysis tools is the main player in the growth of social media. Business analysts can achieve useful insights into how the customer thinks about the services, products, and brands by analyzing different tweets, news articles, and online reviews. Managers for customer support and social media address the issues that are either trending or have gone viral. With the help of sentiment analysis, they forward the issues to the product managers to make important decisions in the future.
Voice of employee’s sentiment analysis
According to a report by the Center for American Progress, the cost of losing an employee can incur about 20 to 30% of the salary. 20% of the employees leave their jobs voluntarily while 17% are fired from their positions. To combat this issue, human resource departments are implementing sentiment analysis to help them improve turnover and enhance performance.
HR managers and workforce analysts help to cut off the employees after understanding what they feel about the company and what they are discussing. HR team members proactively address the pain points by the analytic survey of the employees, slack emails and messages, and other communication methods.
Thus we can conclude by saying that sentiment analysis offers amazing insights on the opinion and feelings of the customer. What are the features that people like or dislike about a product? Which element needs more scope of improvement? How is the customer service of the company? These are examples of a few questions that can be answered via sentiment analysis.