Sentiment analysis is a process of computationally analyzing and identifying opinions and judgments from a piece of text. You can understand if a piece of text is positive, negative, or neutral, based on their sentiment analysis. It is also crucial for you to understand the different types of sentiment analysis to know which one fits the best for your purpose.
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A customer generally reads reviews and does substantial research before purchasing a new product. Analyzing the opinions of millions of customers is more accessible through sentiment analysis. It is essential for market research, monitoring the reputation and progress of the brand and product, etc. You can also judge how loyal your customers are. It is most useful for companies that have to deal with a large number of reviews at once.
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Types of Sentimental Analysis
Knowing the different types of sentiment analysis is essential. You might use sentiment analysis for various purposes, but which one fits your purpose the best?
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Fine-grained sentiment
This analysis gives you an understanding of the feedback you get from customers. You can get precise results in terms of the polarity of the input. However, the process to understand this can be more labor and cost-intensive as compared to other types.
Emotion Detection Sentiment Analysis
This is a more sophisticated way of identifying the emotion in a piece of text. Lexicons and machine learning are used to determine the sentiment. Lexicons are lists of words that are either positive or negative. This makes it easier to segregate the terms according to their sentiment. The advantage of using this is that a company can also understand why a customer feels a particular way. This is more algorithm-based and might be complex to understand at first.
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Aspect-based
This type of sentiment analysis is usually for one aspect of a service or product. For example, if a company that sells televisions uses this type of sentiment analysis, it could be for one aspect of televisions – like brightness, sound, etc. So they can understand how customers feel about specific attributes of the product.
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Read more: Sentiment Analysis Using Python
Intent analysis
This is a deeper understanding of the intention of the customer. For example, a company can predict if a customer intends to use the product or not. This means that the intention of a particular customer can be tracked, forming a pattern, and then used for marketing and advertising.
Different methods are used for these different types of sentiment analysis – while one is rule-based, the other is automatic. Rule-based sentiment analysis is more rigid and might not always be accurate. It involves the natural language processing (NLP) routine. On the other hand, automatic sentiment analysis is more detailed and in-depth. Machine learning is used to decode the feedback provided by each customer. So, there is more precision and flexibility here.
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Wrapping up
Sentiment analysis is a great way to understand what the general opinion of the public is, specific to a company or a product. However, it has its own set of challenges and limitations, which can be overcome if it is used efficiently. Sometimes, it is difficult to understand the tone of the feedback, especially if there are irony and sarcasm involved.
Moreover, some algorithms are complicated and may not produce very insightful results. However, sentiment analysis is an excellent way to get unbiased opinions from customers about several things. It can help companies in a lot of aspects, especially when it comes to marketing and advertising or market research.
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