Text Analytics Tools Evaluation Guide
A world full of data holds no importance compared to analyzing data that helps management executives make critical decisions. Analyzing data has become the need of the hour as we are thrust into tons of vague data. Analytics software such as Microsoft Excel offers simple solutions to simple data problems.
Previously companies conducted painstaking customer surveys to gauge public opinion. However, customers now freely offer feedback through social media posts, online reviews, discussion forums, etc. To measure these sentiments, text analysis software is employed.
Recently, text analytics tools have provided the information needed for a company’s success. This text analytics tools evaluation guide will shed some light on which text analytics tool you should deploy.
What is Text Analysis?
The extraction of valuable and actionable insights from gathered texts is called text analytics. Text analytics tools automatically decode the text into lexical components using natural language processing. The text is then thematically analyzed and grouped based on training data from the analysis software. It can find the frequency occurrence of specific keywords and other targets.
The texts studied include emails, social media posts, blog posts, news clips, and online reviews. These reviews provide a wealth of materials companies analyze to improve their products or services. Denoted as the ‘voice of customer,’ online reviews give us actionable insights into customers’ likes, dislikes, and aversions.
Importance of Text Analytics
Text analytics is an immense process that can catapult any business to success. The only catch here is to use this tool cleverly to cater to your company profile and get actionable insights into your customer’s minds. The company can then use this information to better the product and, in turn, offer the customer the best value for money; therefore, evaluation of text analytics tools is essential.
The data is gibberish if you don’t know what tool to pick for your business while conducting text analysis. Two vital aspects of text analysis help a company listen to the voice of customers. It measures the service capabilities of firms, aka their strengths and weakness, from a customer’s viewpoint.
How is Text Analysis Performed?
Sentiment analysis and topic modeling/categorization are the two types of text analytics techniques. Both techniques have their advantages and disadvantages. Sentiment analysis is the ability of a program to detect a human response in terms of emotions. Meanwhile, topic categorization is training a program to see a text’s themes.
Both techniques rely on modern automation tools based on artificial intelligence. Text mining and text analytics may sound the same as both terms are used interchangeably; text mining uses statistical processes to get results. Meanwhile, text analytics tools offer visualizations of the trends and patterns emerging from the text. Mining tools provide the same purpose as text analysis ones.
Approaches to Text Analytics
Text analytics analyzes texts to extract consumer insights from unstructured and unorganized texts across the web. It helps businesses achieve their set targets and improve upon their services. By connecting the company to its customers, the voice of the customer becomes a focal point in the business’ ever-evolving nature and increases its profits. While there are many approaches to performing text analytics, here are the most used.
Manual Text Analytics
One of the oldest ways to analyze text was to spot words or keyword extraction. It detects certain words that would define a specific theme, for example, the price of a cell phone. Customers may offer reviews about a cellular phone that can be clubbed by memory, processing power, speed, data speeds, cost, etc.
So if you’re performing manual text analysis, you will find words related to pricing such as price, cost, expensive, cheap, and so on. The analysis will be rudimentary at best, analyzing the overall theme crudely. It is only feasible for small businesses with limited diversity in their services.
Rule-Based Text Analytics
Rule-based text analytics is more popular and advanced than spotting words. This analysis technique also relies on identifying connections between adjacent words to get the crux of the sentence or phrase. Sometimes, two words in a vicinity can point to a specific theme. Rule-based text analytics uses adjacent words to help the program find the sentence’s meaning.
For example, a company’s product is talked about in online reviews. A rule-based analysis model can scrutinize the text mentioning this product alongside other keywords such as price, battery size, performance, etc. The program is taught to connect the two words and appoints a score or occurrence frequency. You can put your product’s name in the system to find the desired matches.
Despite its success, the rule-based and manual techniques have several deficiencies:
- Human language is hard to process for humans, and fewer computers. Even human beings struggle to decipher sarcasm from standard language sometimes. Thus, language detection can be tricky sometimes and may result in errors. Double-meaning words or context are required to understand placing a particular positive mention in a negative connotation and vice versa.
- Sometimes a particular word appearing in a phrase may be unrelated to the gist of the text. It may hold less importance than other words, but the computer program may not recognize the difference and thus produce erroneous results.
- Even if the computer program can decipher the phrase’s correct purpose, there’s still a matter of intent or emotion in the text. A company must also perform sentiment analysis and rule-based text analytics to gauge the hidden emos in the text.
- Finally, a company may not accurately update the taxonomy libraries required to perform text analytics for specific service areas. For example, software and tech-related services are relatively new, and the jargon is pretty technical. For a service to assume its text analytics tool’s evaluation will be correct, it must develop an extensive taxonomy library. Maintaining these libraries and continuously maintaining the rules for the program to run smoothly is also complex.
A process whereby a machine learning algorithm learns previously categorized training data to apply it in the future is called text characterization. A machine learning or deep learning model is one in which the program knows through training and applies the principles to the following examples. A truly marvelous way to use artificial intelligence; it uses natural language processing and neural networks to break down the language into processable elements and think of it as a human language but for computers.
When employing machine learning models for text characterizations, the training data must be up to the mark. Many proponents of this technology believe that the machine learning model relies highly on the training examples taught. Not only the quantity but also the quality of this training data is of vital importance to get accurate results. Similar to rule-based analysis, text characterization suffers a few drawbacks:
- The first drawback of the text characterization technique is its specificity in finding themes across the text. It means that themes for which the computer didn’t train the algorithm won’t be picked up by it.
- Secondly, the amount of training data to get maximum accuracy is uncertain. An erroneous algorithm can’t be pinpointed and corrected as only hit and trial method can work to train it.
- Managing the training dataset is time-consuming and costly. Specific expertise is required to achieve this, yet there will be instances where the training datasets will be useless when applied to different departments.
A technique that is a version of the previously mentioned text characterization process, topic modeling differentiates itself from the latter because of its unsupervised manner. Recalling the other approach, which employs deep learning algorithms for text analysis, topic modeling follows the same but without the training material.
The program learns itself and identifies similar scenarios to perform the analysis. The good thing about topic modeling is its disassociation from training datasets. The program applies deep learning algorithms on its own. However, it is also plagued with inaccuracies and needs a human touch to get more accurate results.
The text analytics tools evaluation guide helps the reader understand the importance of text analytics software and how they play a massive part in a company’s success. It has various benefits, from improving productivity, better liaison with customers, and a better approach toward marketing campaigns. Better liaison results in better customer service teams to help customers.
Marketing campaigns are tricky to nail and can become a costly endeavor. However, text analysis solves this problem by telling a company exactly how to capture the customer’s imagination.
Different text analytics tools are suited to specific needs hence a company business should employ these tools carefully and diligently. At VizRefra, we provide the best text analytics tools that are tailor-made for your business.