Sentiment analysis is the process of classifying whether a block of text is positive, negative, or, neutral. Sentiment analysis is contextual mining of words which indicates the social sentiment of a brand and also helps the business to determine whether the product which they are manufacturing is going to make a demand in the market or not. The goal which Sentiment analysis tries to gain is to analyze people’s opinion in a way that it can help the businesses expand. It focuses not only on polarity (positive, negative & neutral) but also on emotions (happy, sad, angry, etc.). It uses various Natural Language Processing algorithms such as Rule-based, Automatic, and Hybrid.
For example, if we want to analyze whether a product is satisfying customer requirements, or is there a need for this product in the market? We can use sentiment analysis to monitor that product’s reviews. Sentiment analysis is also efficient to use when there is a large set of unstructured data, and we want to classify that data by automatically tagging it.Net Promoter Score (NPS) surveys are used extensively to gain knowledge of how a customer perceives a product or service. Sentiment analysis also gained its popularity due to its feature to process large volumes of NPS responses and obtain consistent results quickly.
Why perform Sentiment Analysis?
According to the survey,80% of the world’s data is unstructured. The data needs to be analyzed and be in a structured manner whether it is in the form of emails, texts, documents, articles, and many more.
- Sentiment Analysis is required as it stores data in an efficient, cost-friendly.
- Sentiment analysis solves real-time issues and can help you solve all the real-time scenarios.
Types of Sentiment Analysis
- Fine-grained sentiment analysis: This depends on the polarity based. This category can be designed as very positive, positive, neutral, negative, very negative. The rating is done on the scale 1 to 5. If the rating is 5 then it is very positive, 2 then negative and 3 then neutral.
- Emotion detection: The sentiment happy, sad, anger, upset, jolly, pleasant, and so on come under emotion detection. It is also known as a lexicon method of sentiment analysis.
- Aspect based sentiment analysis: It focuses on a particular aspect like for instance, if a person wants to check the feature of the cell phone then it checks the aspect such as battery, screen, camera quality then aspect based is used.
- Multilingual sentiment analysis: Multilingual consists of different languages where the classification needs to be done as positive, negative, and neutral. This is highly challenging and comparatively difficult.
How does Sentiment Analysis work?
There are three approaches used:
- Rule-based approach: Over here, the lexicon method, tokenization, parsing comes in the rule-based. The approach is that counts the number of positive and negative words in the given dataset. If the number of positive words is greater than the negative words then the sentiment is positive else vice-versa.
- Automatic Approach: This approach works on the machine learning technique. Firstly, the datasets are trained and predictive analysis is done. The next process is the extraction of words from the text is done. This text extraction can be done using different techniques such as Naive Bayes, Linear Regression, Support Vector, Deep Learning like this machine learning techniques are used.
- Hybrid Approach: It is the combination of both the above approaches i.e. rule-based and automatic approach. The surplus is that the accuracy is high compared to the other two approaches.
Sentiment Analysis has a wide range of applications as:
- Social Media: If for instance the comments on social media side as Instagram, over here all the reviews are analyzed and categorized as positive, negative, and neutral.
- Customer Service: In the play store, all the comments in the form of 1 to 5 are done with the help of sentiment analysis approaches.
- Marketing Sector: In the marketing area where a particular product needs to be reviewed as good or bad.
- Reviewer side: All the reviewers will have a look at the comments and will check and give the overall review of the product.
Challenges of Sentiment Analysis
There are major challenges in sentiment analysis approach:
- If the data is in the form of a tone, then it becomes really difficult to detect whether the comment is pessimist or optimist.
- If the data is in the form of emoji, then you need to detect whether it is good or bad.
- Even the ironic, sarcastic, comparing comments detection is really hard.
- Comparing a neutral statement is a big task.