In the contemporary world, where organizations have deployed a huge amount of unstructured data, they always look for innovative ways of structuring, processing, and analyzing this huge volume of data. This data may also be in many forms, including customers’ voices, emails, social networking posts, reviews, and survey feedback. Of the many other forms of unstructured text, a great proportion can be efficiently utilized through text analytics tools.
Text Analytics Tools - Introduction
Text analytics tools enable businesses to extract value from, comprehend, and analyze textual data and convert it into insights. These tools present a combination of machine learning (ML), artificial intelligence (AI), and natural language processing (NLP) services for processing unstructured data. They aid companies in locating vital patterns, trends, emotions, and entities in the text, making it easier for the company to make better decisions, improve customer service, and expand its marketing operations.
The present article aims to provide an in-depth review of the most advanced text analytics tools and software available on the market, describe their capabilities, and demonstrate how to apply them correctly to achieve the highest possible efficiency of your textual analysis.
What Are Text Analytics Tools?
Text analytics tools are intelligent systems or applications that analyze unstructured textual information and generate implications that can be applied to business activities out of the analysis. These tools incorporate AI, ML, and NLP to analyze huge datasets and identify important features, including sentiment, topics, entities, and themes. This enables them to find patterns and trends within the data that may be out of the reach of the human eye.
The focus is on how to automate and streamline large volumes of text data using the following text analytics tools:
- Sentiment analysis: It checks the emotional context of the text, which could be positive, negative, or neutral, depicting the sentiments about a particular product, brand, or service.
- Text classification: Text is classified as having been specified beforehand, including spam and non-spam messages, types of product complaints, and information about customer satisfaction levels.
- Named entity recognition (NER): The identification and classification of particular aspects such as people’s names, organizations, dates and geographies, and many others.
- Topic modeling: The process of determining the ideas or topics contained in a body of material or graphically represented in a set of text documents.
- Keyword extraction: Finding the primary or key terms and phrases mentioned most.
- Text summarization: Producing shorter texts while keeping the main ideas of the very large amounts of written texts in the same volume but with fewer texts.
This enables organizations to analyze enormous amounts of textual data that would otherwise be unmanageable to process manually. They are among the most significant and competitive tools in customer care, marketing, sales, social media, and market research management.
Key Features of Text Analytics Software
When choosing a textual analysis program, there are a couple of things to check. First, there is a set of software requirements. The basic functionalities and features are as follows:
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Natural Language Processing (NLP) Features
NLP can be seen as the core of language analytics in many areas. It allows the tools to parse human language. Text analytics tools rely on NLP methods for tokenization, part-of-speech tagging, dependency parsing, and sentiment analysis. More sophisticated NLP techniques allow the tools to dive deeper by adding more complex analyses, such as sarcasm detection and context comprehension.
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Sentiment Analysis
As mentioned earlier, sentiment analysis is considered one of the most crucial elements in text analytics systems. It enables industries to measure potential clients' dissatisfaction with products, services, or brands by investigating their reviews and feedback. Sentiment analysis tools automate classification for feedback into three levels – positive, negative, and neutral. This gives an organization an accurate view of its clientele’s emotions and feelings. This is very suitable, especially for managing customer experience, examining brand trends, and keeping track of various social media channels.
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Creating Topics with Models and Clustering Together Similar Texts
Text analytic tools can be used to read and compare documents, achieved through topic modeling and clustering. While topic modeling aims at determining the key subjects addressed in a given body of text, clustering, on the other hand, seeks to classify borderline-related text documents. These functions are useful for trend analyzers, problem identification, and data management for easy data mining and retrieval.
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Recognition of Named Entities by NER
NER is used to locate and classify specific information in the source text, such as persons’ names, organizations, locations, dates, and other relevant information. That capability is beneficial for social media, news, and any kind of business intelligence application. For instance, a company can monitor how people talk about its brand, its rivals, and events concerning its area of operation.
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Text Categorization in Text Mining as an Activity
Text classification involves automatically assigning a document to one of the pre-established categories. This feature applies to spam filters, organizing support tickets, or routing emails, depending on the body content. Automating the text classification process allows companies to optimize their processes and cut down on the work that must be done manually.
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Enabling Data Visualization
Data visualization can be regarded as an integral subpart or feature of text analytics software. Visualization tools help reclassify sophisticated, diverse data sets into visual formats such as bar charts, word clouds, heat maps, and pie charts. It becomes easier to detect empirical evidence or indicators of trends, patterns, and anomalies in large text data sets by employing such graphics. Efficient data visualization tools also enable the users to navigate to lower levels of the data for a more complex analysis.
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Support of Different Languages
Since businesses today are not concentrated and rather span the entire globe, multilingual support is essential. Some text analysis software can analyze sales or marketing messages in various languages, allowing companies to obtain customer feedback in different regions. This function is especially important for companies that are actively expanding to new regions and need to measure customers’ reactions in different languages.
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Scalability And Performance Of The Tool
As companies expand, so does the amount of text data. Selecting a text analysis tool that can work effectively with big datasets is important. Scalable solutions allow companies to process a large amount of active data without diminishing returns. These tools should also enable high volumes of text to be processed in real-time or in batches.
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Customization
Every business has specific requirements in terms of text analytics implementation. Customization makes it easy for the business to resolve specific requirements, including creating custom models, modifying workflows, or introducing new data fields. Such and other similar platforms like MonkeyLearn or TextRazor allow businesses to formulate custom models that meet the company’s focus and needs.
Popular Text Analytics Tools
In the following paragraphs, we will examine some of the best text analytics tools that are currently available in the market:
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Kapiche
Kapiche is an AI-centered solution that assists businesses in interpreting qualitative data by providing tools to help understand their clients better. It provides tools for sentiment analysis, topic recognition, and dashboards. Customers can also track satisfaction and trends in an intuitive and real-time Kapiche tool. It is also a great tool for companies of all sizes as it is easily expandable.
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Lumoa
lumoa.com focuses on customer needs and provides qualitative interpretations through sentiment and topic analysis functions. It provides a useful tool for companies that want to analyze responses to customer surveys and other overviews and responses that need to be included. Lumoa’s quick interface and effective reports also allow companies to identify and address issues that matter to customers in no time.
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MonkeyLearn
MonkeyLearn differs from other extractors in that it is a flexible AI text analysis tool. It allows users to create various models based on the services they provide in sentiment analysis, text classification, and keyword searching. It is, therefore, effective for people who want to analyze text data but are more keen on being flexible. MonkeyLearn is very flexible with its simple interface and integration with other apps, such as Google Sheets and Zapier.
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Zonka Feedback
Zonka Feedback is an all-in-one solution for all feedback needs, starting from feedback collection through surveys to analyzing the textual comments provided by the customers. Its key features include sentiment analysis, text analytics, and reporting. Zonka Feedback is well-suited for businesses seeking to enhance their products and services based on customers’ surveys, online reviews, and other inputs.
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TextRazor
TextRazor offers ease of use through API combined with strong text analysis capabilities. Areas of specialization include entity recognition (NER), relation, and sentiment analysis. TextRazor’s API is also particularly useful to developers who seek to perform text analytics on their applications. It also covers multiple languages, which suits companies operating in a wider scope.
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SAS Text Miner
SAS Text Miner is a business-grade text analysis software with advanced text mining, clustering, analytical text mining, and predictive analytics solutions. It is commonly used in finance, healthcare, retail, and other industries with enormous datasets. SAS Text Miner’s sophisticated algorithms can analyze unstructured text data and find previously unknown trends and connections, which can be used in analytics.
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Aylien
Aylien is a powerful text analytics tool that uses various tools, such as sentiment analysis, entity recognition, and text summarization. It is multilingual and provides an API that makes it easy for users to extend text analysis functions to other applications. Aylien is perfect for companies that collect news articles, market data, and social media posts for analysis.
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WordStat
WordStat is a content analysis software aimed at content generated by open-ended survey questionnaires, interactions on social media, and customer self-reported feedback. It has rich functionality in text mining, data categorization, and data visualization. WordStat is most appropriate for organizations that require analysis of unstructured customer feedback data and text responses on various issues.
Choosing the Right Text Analysis Tool Online
Choosing a suitable text analysis tool online may be overwhelming; however, concentrating on a few essentials can make the decision easier.
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Simplicity
Select a simple tool because you will probably have a data science team; for instance, Kapiche and Zonka Feedback are user-friendly. When looking for an online text analysis tool, consider several points to ensure it will satisfy your requirements.
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Implementation and Change
Be on the lookout for tools that let you adjust and modify the analysis according to the needs of the data analytics company, such as MonkeyLearn and TextRazor, where customizable models and workflows are available. Such tools help businesses customize the tool to suit their requirement, for example, creating customized text classification models, establishing criteria for sentiment analysis, or programming the tool to understand the jargon of a particular industry.
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Data Import Needs & Requirements
Considering text analysis requirements, importing information from different repositories, including email, social networks, customer feedback, and even websites, might be necessary. Check whether the text analytics software you have opted for can handle all the mentioned data input methods. More specifically, Aylien and Kapiche would be more suited for such tasks as they have been designed to analyze data collected from several sources.
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Analysis in Real Time
For such businesses, including customer service or social media management, a need arises for software or tools that help with real-time text analysis. A number of these AI-enhanced systems help analyze or process the data to give real-time analysis, letting businesses address problems or prospects as they occur. Other tools such as Lumoa or Kapiche enable AI NLU processing that helps analyze business at real-time trends.
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Cost Versus Features Provided
What is the tool's price, and what are the features provided – having these two questions is clinically important in making the final decision. There are free options such as MonkeyLearn free text analysis tools, whereas more complex ones such as SAS Text Miner and Kaplan are much more advanced and require a subscription fee beforehand. Thus, comparing whether the tool's functioning meets its price is necessary to lead you to the most appropriate decision.
Conclusion: Leveraging The Best Text Analytics Tools for Insight
Text analytics tools have become extremely effective in obtaining crucial insights in this fast-changing world, and businesses with impact working with organizations in text reading areas such as analytics. Thus, whether it is comprehending customer feedback or reviewing key trends in social media platforms, the right text analytics software tool can do plenty of good and help make informed choices while raising the clients' satisfaction.
The best text analysis tools have solid attributes such as natural language processing, sentiment analysis, entity recognition, and visualization. Applications like Kapiche, MonkeyLearn, and Zonka Feedback are well-featured, making them easy for almost everyone to use, irrespective of their level of technical knowledge.
To conclude, text analytics tools are important for making the most of one’s data, and with time as a virtue, the tools will become even stronger than they are. By choosing the best text analysis software, a business can improve the experience of its clients and get a competitive edge over its rivals.
A leading enterprise in Data Analytics, SG Analytics focuses on leveraging data management solutions, predictive analytics, and data science to help businesses across industries discover new insights and craft tailored growth strategies. Contact us today to make critical data-driven decisions, prompting accelerated business expansion and breakthrough performance.
About SG Analytics
SG Analytics (SGA) is an industry-leading global data solutions firm providing data-centric research and contextual analytics services to its clients, including Fortune 500 companies, across BFSI, Technology, Media & Entertainment, and Healthcare sectors. Established in 2007, SG Analytics is a Great Place to Work® (GPTW) certified company with a team of over 1200 employees and a presence across the U.S.A., the UK, Switzerland, Poland, and India.
Apart from being recognized by reputed firms such as Gartner, Everest Group, and ISG, SGA has been featured in the elite Deloitte Technology Fast 50 India 2023 and APAC 2024 High Growth Companies by the Financial Times & Statista.
