How Accurate is Sentiment Analysis?
If you run a business, you will know the significance a customer’s opinion can have on your sales. Nowadays, most companies and organizations take feedback to understand whether their customers are pleased or not. When customer feedback highlights a negative aspect, the company works to improve the situation. However, feedback can range in thousands. So we need to find a system that takes in all the reviews, comments, and posts and analyzes the hidden sentiments within them. It is possible only through sentiment analysis. Systems break down chunks of text and find the positive or negative meaning using natural language processing.
It’s an efficient process, but our concern is the system’s accuracy. After all, your business decisions depend on the results driven by the process. An incorrect result leads to poor future planning. Thus, seeing the accuracy of sentiment analysis should be your primary concern. In the following paragraphs, we explain how accurate the system is, what affects the accuracy level and what sentiment scores are ideal.
Is Sentiment Analysis Accurate?
Sentiment analysis runs on AI and NLP, which help automate the system. It is an advantage for people, but at times, this automation affects the overall accuracy rate of the machine. That is why one cannot say that sentiment analysis is a hundred percent accurate. However, you can train it to get an acceptable result.
Detecting correct sentiments is necessary for your business. Until you don’t know about the client’s opinions, you can’t make any changes or progress. Therefore, check that your analysis system provides an accurate result. For this, one needs to set a standard. According to experts, human analysts can provide 80-85% correct results. So when we are training or testing a sentiment analysis algorithm, we must ensure that our results are near or higher than this value. Several systems in the market give a high accuracy rate of nearly 60%, which is a defendable value.
Sentiment Analysis Score
A sentiment score shows the negativity or positivity in a piece of text. If your score is below -0.05, the sentence holds a negative emotion. Any score above 0.05, has a positive sentiment. In some cases, a system comes across a neutral sentence, which is marked 0. Once the system has given a sentiment score to every piece of text, it calculates a total score and an average value. Depending on the final value, we determine how accurate the result is. The scoring mechanism plays a significant role in the process because the higher the score, the easier it is to find the hidden emotion in a review.
What are the Limitations/Challenges Faced in Sentiment Analysis?
Automatic sentiment analysis methods save us from massive amounts of work. Thanks to a sentiment model, humans do not need to invest energy into collecting and sorting data. You can analyze volumes of data in a short span. After the analysis, you will be aware of customer sentiments. Still, these automated systems do face issues, especially in the following cases:
Sarcastic remarks are difficult to identify. Individuals express negative opinions using sugar-coated words. Therefore, the system is unable to perform its task correctly. Computers do not have human emotions. It is illogical of us to expect, that a system will understand our tone and body language without proper training. So, if your clients write sarcastic comments on your social media pages, the system will show an incorrect analysis.
Some text documents have Multipolarity, which will mislead the system into giving inaccurate results. For example, an average value might not be able to represent significant data points. We will find several comments within a review. Some are positive but not all. They might praise a company’s products but criticize its delivery services. So, it will be a problem.
Ambiguity in a sentence
Having ambiguity in a dataset is another pitfall. It is difficult to determine the polarity of ambiguous words because it’s dependent on the context of the entire sentence.
Presence of Negations
Negations can cause problems for sentiment analyzing mechanisms. Sometimes the effect of negation won’t show up till the very end of the sentence, and it causes problems in polarity.
How to Increase Accuracy?
You might not always get a perfect result from a sentiment analysis model, but that doesn’t mean we abandon the solution. There are ways to improve the overall accuracy of a system. For example, if your text contains idioms or sarcastic remarks, you could use rule-based or statistical analyzers to overcome the challenge. Rule-based models can automatically detect sarcastic tone within a piece of text. Similarly, if negation affects your accuracy score, you should mark negated words with the punctuation token. Last but not least, you can even use lexicon-based sentiment techniques to improve the accuracy of your results.
Which Model is Best for Sentiment Analysis?
There are multiple methods to analyze sentiments in documents and comments. The best technique is to choose deep learning algorithms. With deep learning algorithms, you get high accuracy scores. There are several options like SVM, random forest, and Naïve Bayes. Even logistic regression works perfectly for massive datasets. You can continuously train your model to improve the accuracy percentage.
However, if someone doesn’t want to build a model from scratch, they can avail the opportunity to use cloud sentiment analysis services. Google Cloud Natural and AWS Comprehend are some of the existing solutions. According to research, the combination of AWS Comprehend, Google Cloud Natural Languages, and IBM Watson, gave a 73% accurate result.
Importance and Need of Sentiment Analysis in Today’s Time?
Over the years, sentiment analysis has become a necessity instead of a luxury. Business owners want an insight into their clients’ perceptions. However, no human can get in touch with every customer or open their reviews to read each comment. So, these systems are the best alternative. They check your valuable data, categorize it into negative, positive, and neutral sentiment and show you a consolidated result. Still, accuracy is a matter where every individual must be careful.
Even after many years of hard work, these techniques can’t give us a hundred percent accurate result. It could be due to any reason like Multipolarity or the presence of negations, but that doesn’t mean the results are always incorrect. You can work on the system and use various solutions to increase the accuracy value. Once you employ the correct method, your model will efficiently analyze hidden sentiments and help you make the right decisions for your business.
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