What is Sentiment Analysis?

Sentiment analysis is a popular task in natural language processing. The goal of sentiment analysis is to classify the text based on the mood or mentality expressed in the text, which can be positive negative, or neutral.

Sentiment Analysis

Sentiment analysis is the process of classifying whether a block of text is positive, negative, or, neutral. The goal which Sentiment analysis tries to gain is to be analyzed people’s opinions in a way that can help 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.

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 popularity due to its feature to process large volumes of NPS responses and obtain consistent results quickly.

Sentiment

Why perform Sentiment Analysis?

Sentiment analysis is the contextual meaning of words that indicates the social sentiment of a brand and also helps the business to determine whether the product they are manufacturing is going to make a demand in the market or not.

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.

1. Sentiment Analysis is required as it stores data in an efficient, cost-friendly.
2. Sentiment analysis solves real-time issues and can help you solve all real-time scenarios.

Types of Sentiment Analysis

1. Fine-grained sentiment analysis: This depends on the polarity base. This category can be designed as very positive, positive, neutral, negative, or very negative. The rating is done on a scale of 1 to 5. If the rating is 5 then it is very positive, 2 then negative, and 3 then neutral.
2. Emotion detection: The sentiments happy, sad, angry, upset, jolly, pleasant, and so on come under emotion detection. It is also known as a lexicon method of sentiment analysis.
3. Aspect-based sentiment analysis: It focuses on a particular aspect for instance if a person wants to check the feature of the cell phone then it checks the aspect such as the battery, screen, and camera quality then aspect based is used.
4. 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:

1. Rule-based approach: Over here, the lexicon method, tokenization, and parsing come 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 number of negative words then the sentiment is positive else vice-versa.
2. Machine Learning 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, Support Vector machines, hidden Markov model, and conditional random fields like this machine learning techniques are used.
3. Neural network Approach: In the last few years neural networks have evolved at a very rate. It involves using artificial neural networks, which are inspired by the structure of the human brain, to classify text into positive, negative, or neutral sentiments. it has Recurrent neural networks, Long short-term memory, Gated recurrent unit, etc to process sequential data like text.
4. Hybrid Approach: It is the combination of two or more approaches i.e. rule-based and Machine Learning approaches. The surplus is that the accuracy is high compared to the other two approaches.

Applications

Sentiment Analysis has a wide range of applications as:

1. 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.
2. 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.
3. Marketing Sector: In the marketing area where a particular product needs to be reviewed as good or bad.
4. 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 the sentiment analysis approach:

1. If the data is in the form of a tone, then it becomes really difficult to detect whether the comment is pessimist or optimistic.
2. If the data is in the form of emoji, then you need to detect whether it is good or bad.
3. Even the ironic, sarcastic, comparing comments detection is really hard.
4. Comparing a neutral statement is a big task.

Example of Sentiment Analysis

• Sentiment Analysis of Hindi Text – Python
• Facebook Sentiment Analysis using python
• Twitter Sentiment Analysis using Python
• Sentiment Analysis with Recurrent Neural Networks (RNN)
• Emotion Detection using Bidirectional LSTM
• Sentiment Classification Using BERT