Sentiment Analysis
Sentiment analysis is a technique to analyze a piece of text to determine the sentiment behind it. It combines machine learning and natural language processing (NLP). Sentiment analysis’s goal is to analyze people’s opinions to help the businesses expand. It focuses on polarity (positive, negative & neutral) and emotions (happy, sad, angry). It uses various Natural Language Processing algorithms such as Rule-based, Automatic, and Hybrid.
For example, you can use sentiment analysis to analyze customer feedback. After collecting that feedback through various mediums like Twitter and Facebook, you can run sentiment analysis algorithms on those text snippets to understand your customers’ attitudes towards your product.
Types of Sentiment Analysis
Depending on how you interpret customer feedback and queries, you can define and tailor your categories to meet your sentiment analysis needs. In the meantime, here are some of the most popular 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 a scale of 1 to 5. If the rating is 5, it is very positive, 2 then negative, and 3 then neutral.
Emotion detection: Emotion detection sentiment analysis allows you to go beyond polarity to detect emotions like happiness, frustration, anger, and sadness. Many emotion detection systems use lexicons (i.e., lists of words and the emotion they convey) or complex machine learning algorithms.
Aspect-based Sentiment Analysis: Aspect Based Sentiment Analysis (ABSA) is a technique that considers the terms related to the aspects and identifies the sentiment associated with each element. The ABSA model requires aspect categories and related aspect terms to extract sentiment from each part from the text corpus. One can create a domain-specific model for a specific implementation; however, general language models can also be used.
Multilingual sentiment analysis: Multilingual sentiment analysis can be complex. It involves a lot of preprocessing and resources. Most of these resources are available online (e.g., sentiment lexicons), while others need to be created (e.g., translated corpora or noise detection algorithms), but you’ll need to know how to code to use them.
How does Sentiment Analysis work?
There are three approaches used:
Rule-based approach: Here, the lexicon method, tokenization, parsing comes in the rule-based. The process 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 following 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.
Why Is Sentiment Analysis Important?
Since humans express their thoughts and feelings more openly than ever before, sentiment analysis is becoming an essential tool to monitor and understand the sentiment in all types of data.
For example, using sentiment analysis to analyze open-ended responses in your customer satisfaction surveys automatically could help you discover why customers are happy or unhappy at each stage of the customer journey.
The overall benefits of sentiment analysis include:
Sorting Data at Scale: Manually sorting through thousands of tweets, customer support conversations, or surveys is just too much business data to process manually. Sentiment analysis helps businesses process vast amounts of unstructured data efficiently and cost-effectively.
Real-Time Analysis: Sentiment analysis can identify critical issues in real-time; for example, Is an unhappy customer about to churn? Sentiment analysis models can help you immediately recall these kinds of situations so that you can take action right away.
Consistent criteria: It is estimated that people only agree around 60-65% of the time when determining the sentiment of a particular text. Tagging text by sentiment is highly subjective, influenced by personal experiences, thoughts, and beliefs.
Applications of Sentiment Analysis
Sentiment Analysis has a wide range of applications as:
Social Media: If, for instance, the comments on social media side as Instagram, all the reviews are analyzed and categorized as positive, negative, and neutral.
Customer Service: All the comments in the form of 1 to 5 are done with the help of sentiment analysis approaches.
Marketing Sector: A particular product needs to be reviewed as good or bad.
Reviewer side: All the reviewers will look at the comments and check and give the overall review of the product.
Challenges of Sentiment Analysis
There are major challenges in the 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.
Conclusion
Sentiment analysis is a powerful tool that you can use to solve problems from brand influence to market monitoring. New tools are built around sentiment analysis to help businesses become more efficient. In a soon-to-follow LinkedIn article, my colleague will take you through Explainable AI and Natural language processing.
