Sentiment analysis vs text analysis

May 2, 2022 | Sentiment analysis, Text analytics

For decades, marketing teams and customer service experts have tried to find the most accurate methods to understand how customers feel. The appeal and effectiveness of products and services have been assessed via focus groups, field trials, interviews, and many other techniques. The advent of the digital age, however, has changed the methods people and companies use to share and access information.

Sentiment analysis and text analytics are software solutions designed to change the way information is gathered and understood. Due to the growing popularity of opinion-sharing sites on the Internet such as blogs, review sites, and social media platforms, businesses are presented with new challenges and opportunities to engage their audiences. Proper implementation of analytic solutions guarantees a business is ready for the 21st century.

What is sentiment analysis?

Sentiment analysis is the use of advanced computational linguistic techniques to study subjective meaning of data. This process is significantly beneficial for the analysis of Voice of the Customer (VoC) information. Sentiment analysis is also known by the names opinion mining and emotion AI.

Because human speech doesn’t always have a literal meaning, it can be hard for traditional analysis tools to accurately define context-based cues. By using sentiment analysis software, an AI solution can find the overall positive, negative, or neutral subtextual meaning of a text, drastically improving the quality of analysis results.

For example, a regular analysis tool could be fed the phrase “I just love getting stuck in traffic on a Monday morning” and it would most likely catalog it as portraying a positive sentiment. In turn, the artificial intelligence of a sentiment analysis system will be able to recognize that the phrase is sarcastic by reading into the subtext of the phrase.

This analysis method is made possible thanks to the combined efforts of the fields of linguistics and computer science. Recent technological developments have allowed for the creation of sophisticated linguistic software solutions, including natural language processing (NLP) and the implementation of biometrics.

There is more than one sentiment analysis model and hybrid sentiment analysis methods can be created by mixing two or more approaches. The following are examples of sentiment analysis methods:

  • Rules-based sentiment analysis: This is a bare-bones sentiment analysis solution that doesn’t include any training or machine-learning algorithm. Instead, this approach works under a set of rules that decide whether a body of text is positive, negative, or neutral. Because these rules are known as lexicons, this method is also known as lexicon-based sentiment analysis.
  • Aspect-based sentiment analysis: This type of sentiment analysis searches for data about specific features in a product. For instance, aspect-based sentiment analysis on a new phone model can select information about the quality of the display, the camera, the battery life, the storage space, and every other aspect that influences the customer experience to classify it accordingly.
  • Emotion detection sentiment analysis: With a combination of lexicons and machine learning, this approach segregates data according to the feelings associated with it. Special words and key terms are selected and the algorithm decides if the emotions expressed represent a positive or negative sentiment.
  • Intent analysis: This approach aims to better understand the goals of the customer. For example, analysis can be performed on whether a customer desires a product or not. Customer intention can then be tracked to look for patterns, which in turn can be used to improve marketing and advertising efforts.

The application of sentiment analysis has become massively popular in the marketing and customer service industries. Sentiment analysis tools simplify gathering valuable insights from customer reviews, survey responses, emails, social media content, customer support tickets, and any other piece of subjective text data.

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What is text analytics?

Also known as text mining, text analytics is the discovery of new data through the automatic extraction of information from written resources. Through the use of text analytics, high-quality information can be gathered out of any form of text, from books to online reviews.

The process of text analytics is facilitated by the following procedures:

  • Information retrieval (IR): This is a human-computer interaction (HCI) that occurs when a machine is used to search for information. The retrieved data may come from a wide variety of sources and searches can be based on either full-text data, metadata, or other types of content-based indexing.
  • Information extraction (IE): This task automatically extracts and creates structured information from unstructured or semi-structured data sources. The gathered information may originate from machine-readable documents and other forms of multimedia.
  • Lexical analysis: Also known as tokenization, this process assigns meaning to a sequence of characters by creating a series of tokens. These tokens are strings of code that get assigned to data to classify it.
  • Pattern recognition: Originating from statistics and engineering, modern pattern recognition uses machine learning algorithms to recognize and label patterns in text or speech. Commonly optimized using training data, pattern recognition systems are able to learn and improve the more they are used.
  • Tagging and annotations: A “tag” is an index term that can be assigned to a fragment of data. This is a form of metadata, and it can be used to both describe an item and make it available for easy future retrieval. Likewise, annotations can be associated with a piece of information, allowing the person using the software to add their own custom remarks.
  • Data mining: This process extracts patterns in data sets regardless of their size and complexity by using machine learning, statistics, and database systems. Data mining techniques allow for the automatic or semi-automatic analysis of large quantities of data, which can then be parsed by a pattern recognition solution.
  • Information Visualization: Analyzed data requires a means of visualization for any process of human analysis to be useful. This is because analyzed data is abstract, and software with the capability to create visual representations of it is necessary for humans to properly understand it.
  • Predictive analytics: By using a variety of statistical techniques, predictive analytics contrasts current and past data to make estimations about future developments. Predictive models can be used to exploit patterns in data, allowing companies to identify potential risks and opportunities.

The overall goal of text analytics is to convert text data into valuable insights. By giving a structure to data, time and resources that would otherwise be spent cataloging and analyzing it can be better spent on more productive tasks, For this reason, text analytics has the potential to improve the efficiency of all communications-based operations.

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The relationship between text analytics and sentiment analysis

The main similarity between text analytics and sentiment analysis is that both are processes that use computational linguistic techniques to produce valuable insights from customer data. Both solutions are indispensable tools for a fully integrated customer service management platform.

What’s the difference between text analytics and sentiment analysis?

While both text analytics and sentiment analysis gather data from text and give it a quantifiable value, the conclusions they aim to reach are different. Text analytics extracts relevant information from unstructured text to give it meaning. In turn, sentiment analysis deciphers the emotions expressed by a body of text.

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Different types of analysis

An analysis of a text provided by text analytics can be used to gain information about trending topics and ideas related to the terms being researched. The words are taken at face value, including their grammar and their relationship with each other. Text analysis can also help create a behavioral profile of the customer who brings up certain subjects.

The same text reviewed by sentiment analysis tools will provide information about whether those trending topics are of a positive or negative nature. In some cases, sentiment score doesn’t even have to be related to text data, as some sentiment analysis solutions work with video, audio, and images. Neural networks can be used to give an AI semantic segmentation abilities, allowing it to “see” pictures by assigning lexicons to pixels.

Different understanding of context

Results provided by text analytics are more direct in nature. For example, if a text analytics process performed on a product keeps retrieving the word “broken”, then that’s a clear sign that there may be issues somewhere along the supply chain. However, text analytics doesn’t understand the contextual difference of the word “broken” in the phrases “my product arrived broken” and “this is a great replacement for a broken product”.

In contrast, sentiment analysis will put the emotions behind the text before any specific words. This means that the first sentence of the previous example would be cataloged as negative, while the second would be seen as positive. Sentiment analysis can also look for neutral sentiment, which refers to the product being researched but not containing any emotional undertone.

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Why is sentiment analysis important?

Sentiment analysis is an essential process to better understand the feelings of customers. By using sentiment analysis, companies can assess the success of their marketing strategies in real time, gaining insight into customer psychology and increasing brand recognition.

Implementation of sentiment analysis gives businesses direct feedback on their strengths and weaknesses. Sentiment score can be used as a guide to gauge the effectiveness of customer service and the quality of products, among many other factors crucial for success.

Not only is sentiment analysis useful to understand the feelings of customers, but it can also help assess how different categories of customers react to the same product. A product may be received differently by various subsets of customers, receiving excellent reviews from some and bad ones from others. Sentiment analysis can help determine how different pre-defined groups of customers interact with a product, and what their preferences are.

Text analytics platform

A fully integrated text analytics strategy allows for vast improvements in how any business handles data. The high-precision text analytics platformsystems offered by Semeon empowers managers and employees by supplying them with actionable insights to upgrade the handling of their tasks.

Advanced analysis tools and flexible categorization methods found within the Semeon platform ensure that you have access to the data you need in the specific way you need it. Furthermore, built-in sentiment analysis and multilingual processing tools guarantee that you will better understand your customers’ needs and desires even beyond a language barrier.

AI-powered text analytics

Check out the link below to learn more about our platform. Or give us a call at 1800-630-6000.

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