Sentiment analysis has been the Holy Grail of determining customer opinions for a long time. We all have a certain type of attitude towards the things we experience. It could be as narrow as positive, negative, or neutral, or as vivid as anger, fear, excitement, please, etc.
Sentiment analysis helps you determine these customer sentiments. In this post, we’ll discuss what is sentiment analysis, along with the algorithms used to perform sentiment analysis.
What is sentiment analysis
First, let’s understand what sentiment analysis is. It’s the process of examining whether a piece of content – usually text – is positive, negative, or neutral. Sometimes, it also helps determine the emotion of the content, such as anger, fear, excitement, happiness, etc.
Data analysts in enterprises use sentiment analysis to conduct accurate market research, monitor brand mentions and reputation, and gauge public opinion. When used correctly, sentiment analysis can help brands make informed decisions and enhance their customers’ satisfaction.
Why is sentiment analysis important
As everything is going digital, people have started talking about brands online. Sentiment analysis offers opportunities to understand what people across various digital channels are talking about their brand, competitors, or other issues and topics.
Sentiment analysis paves the way for social media listening, an important technique for brands to understand their customers’ feedback and opinions. Using this feedback, brands can understand what they’re doing wrong and can make the necessary upgrades to their products or services.
Twitter is one of the most common platforms for sentiment analysis. Most brands have Twitter handles, and a lot of customers use Twitter to express their views – positive or negative – about a brand.
Besides customer feedback and opinion, sentiment analysis can help determine trends in the stock market. Positive sentiment on a large scale will likely surge the prices, whereas negative emotions will plunge the prices.
Sentiment analysis algorithms – how it works?
Now comes the question, “how to perform sentiment analysis.” Well, before delving into the actual method, it’s crucial to understand how sentiment analysis works.
Sentiment analysis utilizes various algorithms, which are broadly classified into two categories:
1. Rule-based approach
Rule-based sentiment analysis refers to a type of sentiment analysis based on an algorithm that clearly defines the opinion. This includes subjectivity, subject, or subjectivity of the opinion.
This algorithm uses the standard natural language processing (NPL) capabilities. The following steps are involved in analyzing a piece of text using the rule-based approach.
- Tagging part of speech
- Lexicon examination
The algorithm comprises two lists of words – one indicating positive emotions and the other indicating negative emotions. When the algorithm analyzes the text, it checks for words in the two lists. If more words from the “positive” list appear in the text, the overall sentiment is marked as positive.
Similarly, if the text contains more words from the “negative” list, the overall sentiment will be considered negative. And if the text contains an equal percentage of words from both the lists, the overall text is recognized as “neutral.”
The rule-based algorithm is good if you only want to know the positivity or negativity of the text. These algorithms are not precise and flexible. Additionally, the chances of the rule-based algorithm delivering inaccurate results are high.
2. Automatic sentiment analysis
Consider automatic sentiment analysis as the advanced version of the rule-based approach. This algorithm digs into the text’s real meaning and context and helps you determine the content’s real sentiment.
Automatic sentiment analysis utilizes machine learning capabilities to determine the sentiment of the text. This leads to a drastic increase in the accuracy of the processed information.
The procedure includes the use of supervised and unsupervised machine learning algorithms. Automatic sentiment analysis involves the following algorithms:
- Naive Bayes
- Linear Regression
- RNN derivatives GRU and LSTM
- Support Vector Machines
The procedure of automatic sentiment analysis is more or less the same as the rule-based approach. It just uses more advanced algorithms to explore deeper layers of the sentiments of a text.
3. Hybrid approach
A third way – hybrid approach – is becoming an increasingly popular sentiment analysis method. It involves the combination of the rule-based approach and ML-enabled automatic sentiment analysis. As a result, this approach helps you determine both the narrow context as well as the deeper context of a text.
The hybrid approach uses a combination of lexicon examination, tokenization, syntax analysis, and linear regression to determine the sentiment of a piece of content accurately.
Back in the day, sentiment analysis was a buzzword applicable only to data scientists in the IT industry. But with brand-customer interactions becoming increasingly digital, brands are looking for ways to determine their customers’. This has lead to the rise of sentiment analysis across all industries.
Earlier, sentiment analysis was a complex task that required intricate coding. But now, software solutions are available that automate the sentiment analysis process.
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