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What Is a Sentiment Analysis Tool?

Sentiment analysis is an invaluable asset for market research and customer experience (CX) leaders, enabling them to obtain penetrating insights into the attitudes, feelings, and emotions of their markets and customers. But what exactly is sentiment analysis? How does it work? And how can you choose the right sentiment analysis tool for your needs? Let’s take a look at all these questions by exploring “what is a sentiment analysis tool?” in greater detail. 

What is Sentiment Analysis?

Sentiment analysis is the process of analyzing text data to identify and extract subjective information such as opinions, emotions, and attitudes. It is used by market researchers and customer experience leaders to gain insights into consumer behavior. Sentiment analysis can be applied to any type of textual data source including online reviews, survey open ends, social media posts, emails, chats, etc.

The benefits of using sentiment analysis are numerous. By automatically extracting sentiment from large volumes of text data it enables businesses to quickly understand their customers’ needs and preferences in order to make informed decisions about product development, operations or marketing campaigns. Additionally, sentiment analysis tools helps reduce costs associated with manual labor for manually processing customer feedback or survey responses.

Sentiment analysis can take various forms, including polarity-based (positive/negative), emotion-based (anger/joy/sadness) and intent-driven (buyer intent). Polarity-based sentiment analysis identifies whether a statement expresses a positive or negative opinion while emotion-based sentiment analysis looks at the emotional tone behind the words being used in order to determine how people feel about something. Intent-based sentiment analysis focuses on understanding what action someone wants to take based on their comments or feedback; for example, if someone writes “I want this product” then they have expressed an intent to buy that product.

Sentiment analysis is a potent tool that can be utilized to obtain insight into customer sentiment and market movements, enabling businesses to take informed decisions. By understanding how it works, marketers can use this technology to their advantage and capitalize on its potential.


How Does a Sentiment Analysis Tool Work?

Sentiment analysis is a process of extracting and interpreting the emotions behind text-based data. It can be used to gain insights into customer experiences, brand perceptions, and market trends. Sentiment analysis tools are devised to process volumes of unstructured data from diverse sources, e.g., social media platforms, reviews on the web, surveys and so forth in order to comprehend the sentiment behind them.

The 3-step process of sentiment analysis generally involves procuring data from disparate sources, implementing NLP to discern context and ML algorithms for categorizing each bit of text in terms of its emotion.

In the initial phase, info is gathered from multiple sources such as social media posts or online reviews. This raw data typically needs to be “cleaned” before any sort of analysis can take place, with superfluous information and disturbances removed in order to guarantee the accuracy of outcomes (though tools like Canvs AI automate data cleaning by automatically identifying SPAM or keyboard mash responses).

In the subsequent step, NLP strategies are utilized to acquire significance from textual content and gain a more profound comprehension of its context. These include tokenization which divides sentences into individual words or phrases; part-of-speech tagging which assigns each word a specific part-of-speech label; named entity recognition which identifies entities such as people’s names or places within a sentence; and sentiment lexicons that assign numerical values based on positive/negative sentiments associated with certain words or phrases in a given sentence.

At last, ML models are used on the extracted attributes to distinguish each text according to its sentiment be it positive, negative or impartial and other characteristics such as intensity of emotion (e.g., very pleased vs a bit happy). The output generated by these algorithms can then be used for further analysis such as identifying customer pain points or tracking changes in public opinion over time about particular topics related to your business operations.

Canvs utilizes a proprietary and patented sentiment measurement model comprised of 42 nuanced emotions. We believe this approach is superior to basic positive/negative sentiment as it provides more details about how the consumer is actually feeling, which can then be mapped to positive/negative according to the context of the reserach and business (e.g. “weird” may be a positive emotion for a funky fashion brand).

Sentiment analysis tools provide an automated, efficient and reliable way to analyze customer feedback. Businesses can exploit the power of NLP and ML algorithms to gain instantaneous insights from customer feedback, thus allowing them to make data-driven decisions regarding their customers’ experiences.

Key Takeaway: Sentiment analysis applications empower organizations to comprehend customer experience, brand perceptions, and buyer patterns by utilizing AI-powered NLP strategies and machine learning to assess large volumes of unstructured data.

What are the Benefits of Using a Sentiment Analysis Tool?

Sentiment analysis is a powerful tool for market researchers and customer experience leaders. Sentiment analysis is a useful technique for market researchers and customer experience professionals, enabling them to quickly assess large amounts of data from varied sources such as text, social media posts, reviews or surveys. Sentiment analysis can provide companies with useful information about how their customers perceive their offerings.

Automation and efficiency can be achieved through the use of sentiment analysis tools that utilize NLP and ML approaches to detect patterns in customer responses. This helps companies save time by reducing manual labor associated with traditional methods of data collection and analysis. In addition, sentiment analysis tools can be used to track changes in consumer opinion over time so that businesses can better understand trends in customer satisfaction levels.

Improved Accuracy and Reliability of Results:

Using sentiment analysis tools also improves the accuracy and reliability of results because they are able to detect subtle nuances in language that may not be obvious when manually reading through comments or reviews. For example, a sentiment analysis tool might be able to differentiate between someone who is simply expressing an opinion versus someone who has had a negative experience with a product or service due to poor quality or lack of support from the company itself. This level of detail would otherwise require significant effort on behalf of the researcher if done manually without any automated assistance.

Finally, using sentiment analysis tools can help businesses save money by eliminating costs associated with hiring additional staff members for manual research tasks such as collecting survey responses or reviewing comments on social media posts. Furthermore, sentiment analysis tools can drastically reduce the time needed for data processing, providing quick results in comparison to manual methods which may take a substantial amount of time depending on the scope. With 88% of business executives saying their company can’t keep up with the pace of consumer change (according to a 2022 Accenture study), accelerating the consumer feedback loop is critical to success.

Sentiment analysis tools can provide a wealth of insights into how customers feel and why, allowing organizations to make informed decisions based on reliable data. Through analysis of multiple data sources, like textual content, social media posts and online reviews/surveys, businesses can gain a deeper understanding of their customers’ desires and tastes.

Key Takeaway: Sentiment analysis tools are invaluable for market researchers and CX leaders, as they provide accurate and reliable insights into customer sentiment quickly and cost-effectively while eliminating manual labor.

What Types of Data Can be Analyzed with a Sentiment Analysis Tool?


Textual data sources are one of the most common types of data that can be analyzed with a sentiment analysis tool. This includes any type of written text such as online reviews, survey open end responses, blog comments or forum posts. These texts often contain valuable information about customer opinions which can be used to inform product development decisions or marketing strategies.

Social media platforms are another great source for gathering customer feedback through sentiment analysis tools. Companies can utilize sentiment analysis tools to survey public opinion on their brand, content or offerings by tracking Twitter, Facebook, Instagram and YouTube comments. Moreover, staying abreast of industry-related trends and consumer inclinations can be achieved by keeping an eye on conversations associated with relevant topics.


Online reviews and surveys are important sources for understanding how customers feel about a business’s offerings, as they provide direct feedback from people who have already experienced the product/service first hand. Surveys can offer a glimpse into the opinions of prospective customers prior to purchase, aiding in pinpointing areas that need improvement for them to opt for one company’s product/service over another. Sentiment analysis tools allow businesses access valuable insights from both online reviews and surveys quickly, so that appropriate action can be taken without wasting time manually sifting through large amounts of unstructured textual data sets.

Sentiment analysis tools can provide valuable insights into consumer opinions and preferences, helping market researchers and customer experience leaders gain a better understanding of their target audience. With the right tool in place, organizations can effectively analyze textual data sources, social media platforms, online reviews and surveys to get an accurate picture of sentiment around their products or services – so it’s important to choose one that meets your specific needs.

By utilizing NLP strategies and ML algorithms, sentiment analysis applications can promptly assess substantial amounts of textual information from sources such as web-based media sites, reviews, polls, etc.

Identifying Your Requirements:

Before choosing the right sentiment analysis tool for your needs it is important to identify what you need from the tool. Consider factors such as accuracy of results, cost savings potentials or time savings opportunities that may be available with certain tools. It is also important to consider if the tool will integrate with existing tools you have in place, such as your research platform (like Qulatrics or Decipher) or your reporting applications, like Tableau and Power BI.

Evaluating Different Tools on the Market:

Once you have identified your requirements it is time to evaluate different tools on the market. Researching each product’s features and capabilities can help narrow down which one best meets your needs while staying within budget constraints. Look at customer reviews or case studies to get an idea of how other companies are using these products successfully before making a decision.

Many sentiment analysis tools offer different pricing models depending on usage levels, text volume or number of users required by your organization. It is important to understand all costs associated with any subscription model before committing, in order to avoid unexpected invoices due to hidden fees or additional charges not discussed up front during negotiations.

Key Takeaway: Sentiment analysis tools can be a valuable asset to gain insights into customer opinions, but it is important to evaluate different products and understand all costs associated with any subscription model before committing.

FAQs in Relation to What is a Sentiment analysis Tool?


What is an example of sentiment analysis?

Sentiment analysis is an NLP technique that applies algorithms to text for the purpose of discerning sentiment or emotion. For example, if someone posts on social media “the product absolutely rocks,” advanced sentiment analysis would recognize the emotion “love” associated witht the comment. Conversely, if someone posted “This product is terrible”, sentiment analysis would identify the emotion of “hate” or “dislike.” By analyzing customer feedback in such ways, market researchers and CX leaders can gain valuable insights into how customers feel about their products or services.

What is sentiment analysis in simple terms?

Sentiment analysis is a process of examining customer feedback and other data sources to discern people’s attitudes towards a certain product, service or subject. It uses natural language processing (NLP) techniques to identify and extract subjective information from text-based sources such as reviews, comments, tweets, surveys, etc. Sentiment analysis provides organizations with an invaluable opportunity to understand how their customers are feeling, thus allowing them to make informed decisions concerning their goods and services. Sentiment analysis can help businesses understand customer sentiment on an individual level as well as across larger groups of customers for more accurate market research results.

How does sentiment analysis works?

Sentiment analysis is a technique of text analytics which uses natural language processing (NLP) to identify and draw out subjective info from customer feedback. Sentiment analysis utilizes NLP to identify and separate opinionated data from customer reviews, surveys, social media posts, emails etc., to determine if they are favorable or unfavorable. Sentiment analysis can be used to gain insights into customers’ feelings about products or services as well as their overall experience with a company. This data can then be used for market research and CX initiatives such as improving customer service, product development and marketing campaigns.

How do you do sentiment analysis?

Sentiment analysis is a process of extracting and analyzing opinions from text data. It involves using natural language processing (NLP) techniques to identify, extract, and quantify the sentiment expressed in a piece of text. This can be done by assigning polarity scores to words or phrases within the text, such as positive or negative. The score assigned will then determine the overall sentiment expressed in that particular piece of text. Advanced approaches use artificial intelligence (AI) to identify the emotion being expressed in the text to provide a more detailed and nuanced undertanding of how consumers feel.


Sentiment analysis tools help to quickly analyze large amounts of data, uncover hidden insights, and provide valuable feedback on customer experiences. With the right sentiment analysis tool, businesses can gain invaluable insight into their customers’ needs and preferences. By using a sentiment analysis tool, companies can make better decisions that lead to improved customer satisfaction and increased profits.

With Canvs AI, you can get deep insights into the sentiment of your customers and stakeholders. Our advanced, AI-powered text analytics solution helps organizations transform open-ended text into valuable consumer insights. In addition to sentiment analysis in the form of detailed emotion measurement, Canvs automatically identified statistically significant trends and correlations in the data to dramatically accelerate time-to-insights, reduce costs and increase “return on insights” (ROI). 


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