Introduction to Sentiment Analysis

How does Sentiment Analysis work?

Sentiment Analysis will be done in three ways:

1. Rule-based approach: In this method, certain rules will be crafted, and the statements or text will be evaluated based on those developed rules. For example, counting as a scheme. Then for a given text, we will count the number of positive terms and negative terms. And then, we will set a rule like if the no. of positive words is greater than that of negative words, then it is classified as positive sentiment and vice versa. This method includes the lexicon method, parsing, tokenization process, etc., to complete this process.

2. Automatic Approach: Automatic approach involves the use of machine learning algorithms such as Naive-Bayes, Linear Regression, etc., to train on the input text and validate the results. This process will include many steps such as tokenization, word cloud building, etc.

3. Hybrid Approach: This method is introduced to increase the accuracy of the model in sentiment analysis. This is the combination of both Rule-based and Automatic approaches.

Uses/Applications

Sentiment Analysis is mostly used in the following fields.

• Social Media Monitoring
• Customer Services.
• Product Analysis.
• Market Research.
• Reviewer Side.

1. Social Media Monitoring: Social Media is a huge place where billions of people spend their time seeing other people's information. This includes commenting, replying to a thread, sending an emoji reaction, etc. We can categorize these as positive, negative, or neutral by using sentiment analysis and improving our feed.

2. Customer Services: Customers are very important for a firm or for a company. Customers create a purpose. Customer opinions are very important in developing a product or service. Thus in servicing a customer, knowing their reviews or opinions is very important. For this, sentiment analysis comes in handy. Most companies will create surveys to collect customer opinions to know how their product is?

3. Product Analysis: As we already discussed, its initial reviews are very important to the growth to develop a product. This can be done by using the concept of sentiment analysis. Creating survey forms about the product and fetching the user reviews about the product will improve their product growth.

4. Market Research: Imagine you are about to buy a product. Market research is very important in this case. Like searching the same product on different platforms for acquiring more user feedback about that product, how good it is? What are the faulty things about it, etc., will help you to get a brief idea of that product.

FAQs

1. What is sentiment analysis?
   Sentiment Analysis can be defined as the process of discovering people’s opinions, emotions, and feelings about a product or service. Sentiment analysis is also called Opinion mining. Sentiment Analysis mainly looks at the polarity of the sentences and their intentions.
2. What is the automatic Sentiment analysis method?
   Automatic Sentiment is quite the opposite of the Rule-based method, wherein we don't create rules in this method. Instead, we will depend on machine learning techniques. Generally, this sentiment analysis concept best suits a classification task.
3. Why is sentiment analysis the most interesting research area?
   Sentiment Analysis, also called Opinion mining, has become a very important research area because it is used in so many industries such as the marketing field, commercial sectors, and many other applications. This will help humans a lot.
4. What are the challenges in sentiment analysis?
   If the data is in the form of a tone, or if the sentence is in a neutral state, then it will become a difficult task. In irony or sarcasm, people express negativity by using positive words. This will be a challenging task in this analysis.

Key Takeaways

So far, we have discussed the basics of sentiment analysis, what the concept is, how it works, what some examples are, and their use-cases. We will explore this concept more in detail in the upcoming articles. Thank You!
Check out this problem - First Missing Positive 

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