Sentiment Analysis

Sentiment analysis refers to analyzing various digital texts to determine the author's tone. It is helpful in ascertaining tone, helping boost customer service and augmenting brand reputation. In today’s internet-driven business world, companies have a large chunk of data in digital form. These include social media comments, reviews, emails, and customer support chat transcripts.

Sentiment analysis data helps understand whether the tone of digital text or data is neutral, positive, or negative. What seems like a small insight helps companies devise better marketing strategies, curate better products or services, and produce real-time results. This process is also commonly referred to as opinion mining.

Sentiment analysis or opinion mining is analyzing blocks of a company's digital texts to understand how the consumer perceives them. It not only focuses on polarity factors such as negative, positive, or neutral but also considers emotions such as anger, sadness, or happiness. 

The analysis is performed through multiple Natural Language Processing mechanisms and algorithms, including automatic, hybrid, and rule-based formats. As a result, all digital data, including emails, customer support chats, social media posts and comments, articles, and documents, are structured.

Sentiment analysis tools can considerably differ depending on the type of product or the nature of the industry. Experts use different types of analysis depending on the situation's requirements. Some of the most popular types are multilingual, aspect-based, and emotion detection analysis, which shall be discussed in detail subsequently. 

In fact, even within the same company, the usage of this analysis could differ for different activities or blocks of text. For instance, the usage of the analysis for marketing could be completely different from the usage of customer service or social media. 

Therefore, it is essential to understand the vitality of the tone of communication and gauge what the audiences want to hear and in what format. Once that is figured out, marketing and communication with target audiences become much more manageable. 

Now that we understand the outline of the concept, one thing becomes evident: sentiment analysis is much deeper and layered than what one perceives when hearing the term for the first time. The details of each of its aspects shall be discussed in the upcoming sub-heads. 

The four main approaches used in sentiment analysis projects are:

• Neural Network: In recent years, the use of neural networks has evolved at the rate of knots. Artificial neural networks are designed just like a human brain. Experts use techniques like Gated recurrent units, recurrent neural networks, and long/short-term memory to process texts and determine if they are perceived as neutral, positive, or negative. 
• Rule-Based: The rule-based approach takes into account the number of positive and negative words in a given block of data. Then, it uses methods such as tokenization, parsing, and lexicon. As a result, the company can understand if the number of positive words is higher than the negative words or vice versa to determine the effect of the text on the audience. 
• Machine Learning: The ML approach trains data sets (blocks of text, in this case). After training, experts conduct predictive analysis based on which text is extracted from the dataset. The extraction is performed using techniques like support vector machines, hidden Markov models, and Naïve Bayes.
• Hybrid Approach: As the name suggests, it combines multiple approaches. It is used to increase accuracy and find more common ground. 

The most common types of sentiment analysis tools are:

• Emotion-Detection Analysis: Tools assign feelings like anger, sadness, happiness, excitement, and frustration by attaching words to a correlating emotion. The technique works accurately most of the time. However, it might lapse in accuracy with some colloquial terms. 
• Graded Analysis: It is one of the fundamental methods of performing sentiment analysis. It is a simple rating scale with 1-5 or 1-10 on them. In some cases, numbers are replaced with experience-quantifying terms such as “poor” or “excellent.”
• Fine-grained analysis: This type of analysis breaks sentences down into smaller parts and performs detailed analysis. As a result, it can determine the usage of the best possible combination of words to arrive at the best tonality. 
• Aspect-Based Analysis: This is similar to fine-grained analysis, as it also finds positive or negative sentiment in the dataset. It is best suited for functions such as chatbots, where words can be used to understand a customer's situation and come up with an appropriate response. 
• Intent Analysis: As the name suggests, intent analysis determines whether a given statement or dataset is an opinion, question, complaint, statement of appreciation, or news. It could be an effective way to sort out emails and other forms of communication. 

Sentiment analysis data is used across different industries for various purposes. Some of the most common use cases are:

• Brand Awareness is used to monitor a company's brand awareness, popularity, and reputation over different time frames. 
• Customer Reception: When a new product is launched, sentiment analysis can be applied to learn how customers have received the process to understand the potential improvements to the product or services. 
• Campaign Evaluation: Once a marketing campaign is launched, analysis can be used to determine its effect. For instance, a net promoter score could show how many members of the targeted audience would recommend the product or service to a loved one. 
• Market Research is an effective way to gauge the market and find emerging trends. It can also help with competitive and competitor insights. 
• Service Automation: These types of analysis can help categorize customer complaints based on urgency or seriousness, automate the process accordingly, and minimize the need for human intervention. 

The concept's theoretical aspects are well-established now. It is time to explore its practical application further. The examples below show how these sets of analyses can be used in real-life scenarios. 

Example #1

SwissBiz is a watchmaking company that recently launched a new series of watches targeted at GenZ. Initially, they catered to an older customer base but realized the potential and moved into making watches for the younger population. 

Therefore, they also initiated a social media campaign to launch and promote these watches. They chose social media platforms because most of their target customers could be easily reached there. 

After the watches were launched, the marketing teams performed sentiment analysis to analyze social media comments on the product’s posts. They found that 75% of the comments were positive, 15% were negative, and 10% were neutral. They were able to make subtle changes to their tone and marketing post-analysis, which increased their acceptance by over 85%.

Example #2

According to Kartar Saxena, the Vice President of the Strategic Consulting Group, sentiment analysis helps companies access an extensive range of datasets from distinct communication channels in real-time. As a result, they can improve what has grown in popularity among businesses: Customer Experience (CX).

Understanding customer emotions in a product, service, or marketing campaign has been revolutionized, especially after the introduction of artificial intelligence (AI) and machine learning (ML). Moreover, the measurement of CX has become more objective. 

A few of the most critical benefits of sentiment analysis data are:

• Turns unstructured data into structured data in a way that is useful for analysis.
• It is an effective way of collecting real-time feedback and making improvements to products, services, or marketing campaigns much more effortless.
• The analysis helps find emerging market trends and also helps with competitive analysis of the target market.
• It gathers real-time data that keeps the customer support team updated enough to provide appropriate responses to customers. 
• These systems identify, segregate, and address negative feedback before it escalates to unrepairable levels. 
• The criteria for analysis are consistent and objective instead of entirely relying on subjective human analysis.
• It frees up the company’s resources, including time, effort, and money. 

In spite of mind-boggling developments in the latest sentiment analysis projects, there are still a few areas of concern that still need to be solved. A few of the biggest challenges in this regard are:

• The inability to understand certain human emotions is still a significant challenge. For instance, sarcasm can be difficult for even humans to detect in a text format as the tone is out of the picture. Machines find it challenging to recognize and address such emotions appropriately. 
• While common words run through the algorithm and are trained, new slags or colloquial terms could need to be clarified for the analytical tools and might not deliver the desired results. 
• If a customer writes a lengthy review, there might be more than one emotion embedded in it. Therefore, it might be a challenge for the system to categorize the review appropriately. 
• The increased use of emojis or emoticons makes it essential for experts to run them through the system to determine what a particular emoji signifies. 
• One of the most challenging tasks is to categorize a comment, feedback, or email when there is a lack of emotion. In simple terms, neutral tones/emotions are challenging to categorize and address appropriately. 

The distinctions between sentiment analysis tools and semantic analysis are:

Sentiment Analysis

• It is the evaluation of words in digital formats to understand what the consumer was feeling at the moment they were said. It helps with understanding brand perception and bettering products or services.
• Such analysis is helpful in various business aspects. It can help with marketing, decision-making, sales, and determining campaign success, among other things.
• Using this analysis, customer support becomes a breeze, as the system categorizes complaints and feedback according to their seriousness or urgency. 
• It helps maximize the effective use of a company's resources. 
• Despite the positives, the analysis can sometimes miss emotions or mannerisms such as sarcasm, colloquial terms, or slang. 

Semantic Analysis

• It is the process of determining the fundamental meaning of a particular dataset. As a result, computers and systems can interpret the data and determine the correlation between the words and context.
• It helps with understanding customer inclinations and making necessary changes to reduce the gap between customer expectations and the product/service.
• Semantics also help ensure that the SEO optimization strategy is updated according to the most recent semantic analysis data.