10 Best Text Analysis Software Tools in 2024

Quickly convert your unstructured text data into perfect format with these Top Text Analysis Tools. Pick the best Text Analysis Software of your choice to gain valuable insights from open-ended text:

The organization creates an inventory of data assets by leveraging data management tools and metadata. With the help of data catalog tools, information can be quickly searched and accessed.

Text analysis is extracting facts and figures from the texts. By slicing and dicing heaps of unstructured data, easy-to-manage, understandable, and interpretable information can be fetched. The system can extract valuable and organized content from the unstructured data.

Text Analytics Software: Top-Trending List

Leveraging the text analysis software, classifying, sorting, and extracting information from the texts can be done independently. Text analysis processing requires annotators, feature structure, type, type system, analysis results, and common analysis structure for deep analysis of data.

Who is this Article for?

The users directly or indirectly gathering large quantities of data and finding meaningful insights through it are the target audience for this article. Specifically, for the IT sector team, i.e. data scientist, data analyst, and other marketing team, it is highly useful.

Through this article, readers can acknowledge the actual power of data. People in business or the corporate world should understand how to leverage data and derive fruitful results from it.

How to select the Right Text Analysis Software?

Before selecting the text analysis software tools, a few steps need to be followed:

What is Text Analysis

Statistical, machine learning and linguistic techniques process a large volume of unstructured data to derive useful insights and patterns. Large companies analyzed the text-based data to understand customer and market feedback.

Through text analytics tools, the data is processed in an unbiased manner. The entire process is scalable, consistent, and transparent. The tool analyzes the data and extracts valuable information through the texts.

Here are some of the main ways through which the text analysis can become fruitful:

• Enhance the Content: By simply organizing or by giving the content an outline, the content can be enhanced. After enhancing the content, it can be used in a wide number of applications and on several platforms.
• Efficient Customer Service: Through this technique, automatic response is given to the customers. To enhance the experience of users and for quick resolution of problems, the survey software is highly recommended.
• Sentiment Analysis: With the help of artificial intelligence, positive, negative, or neutral opinions can be known. As a result, the sentiments can be perceived in diverse ways. From a document to a sentence and sub-sentence, the attributes are extracted for obtaining insights into a particular product or service.
• Text Classification: The text can be classified into different categories. Using text classification, the text can be identified as per the category. In topic identification, according to different categories, the text is divided. For example, the topics can be sports, technology, etc.
• Opinion Mining and Opinion Summarization: It is identifying and extracting subjective text from a large chunk of data. Opinions, appraisals, and evaluations are the information that can be extracted from the data. Opinion summarization is the method of extracting only the main sentiments or opinions from the text.
• Search and Retrieval: Through search and retrieval, the intent of the user through the query is searched. For example, a user search for Apple. Then it can be a fruit, laptop, mobile, etc. By leveraging the search mechanism, all the searches are listed down in the results.
• Natural Language Processing (NLP) and Machine Translation: Text analysis can improve accuracy. Through semantic analysis, we can understand the meaning of the question. After that, syntactic analysis identifies the keywords. Following it, the intent behind the question can be understood by pragmatic analysis.

For understanding the human text, with the help of computer systems, text analysis is used. Leveraging it, the information can be extracted, classified, and sorted from huge piles of unstructured data. With the help of text analysis, useful information can be gained quickly as the process is entirely automated.

Text analysis can reveal valuable information such as insights, patterns, and sentiments that can be acted upon. Specifically for emails, reviews, social media content, and highly important documentation, text analysis can assist a lot in figuring out the right things at the right time.

According to the dataset, there are different text analysis:

• Entity Recognition: By this type of text analysis, the names, places, time periods can be recognized from the data.
• N-grams: It is a common two, or three phrases of words in a text.
• Word Frequency: It is a list of words and frequencies of words appearing in a sentence, paragraph or document.
• Collocation: This text analysis defines the words that appear near to each other in the text.
• Concordance: A particular set of words or context of a given word is defined by concordance type of text analysis.
• Dictionary Tagging: A particular set of words in the text or document.
• Clustering: A vast quantity of unstructured data can be grouped through clustering algorithms. For the ease of understanding the data, clustering is done. Clustering algorithms are faster to implement and do not require the training data.

Text Mining

Text mining tools help in analyzing the text for businesses through social media, emails, reviews, feedback, and other content available openly in the market. By gaining these valuable insights, businesses can make the right decisions for the future. Following the process of tokenization, the text mining process is processed.

In text mining, the base word is extracted from the text. Leveraging natural language processing and artificial intelligence tools and techniques, the text is made readable, and deep analysis of text is done.

After that using different softwares like IBM Watson, MonkeyLearn, Amazon Comprehend, MeaningCloud, GoogleCloud NLP, Thematic, and AYLIEN etc. the extraction of text for gaining useful information is performed. With the help of these tools, you can upload and integrate the data directly with the application or excel sheet.

With a very small amount of coding, the APIs are connected with all major languages for text mining.

Text Analyzer

Using an advanced text analyzer enables a more thorough and detailed analysis of text. By incorporating additional statistics, such as word frequency, word length, and common phrases, a more thorough analysis of the text is conducted.

For understanding the text statistically, some standard versions assist in the readability and complexity of a text.

Below are some standard points that are acknowledged while doing text analytics:

• Character per word
• Number of characters
• Number of sentences
• Words per sentence
• Number of paragraph
• Number of different words
• Syllables
• Syllables per word
• Total word count
• Number of different words
• Total word count (Excluding common words)

In the analysis of words by length, a list of words according to the length is grouped. Below is the table created for word length count:

List of the Best Text Analysis Software

Check out the list of exceptional Text Mining software below:

1. MonkeyLearn
2. IBM Watson
3. Voyant
4. Google Cloud NLP
5. Thematic
6. Amazon Comprehend
7. Aylien
8. Chattermill
9. Microsoft Azure
10. SAS Visual Text Analytics

Comparison of the Top Text Analytics Tools

Detailed reviews:

#1) MonkeyLearn

Best for visualizing customer feedback through a clean and simple dashboard.

For analyzing text and finding useful insights from the text, MonkeyLearn is used. With the ease of using machine learning techniques and being a powerful tool, higher levels of accuracy results can be gained through MonkeyLearn. Filtering and combining multiple data inputs is the best feature of MonkeyLearn.

MonkeyLearn offers several enhancing features, such as importing the dataset, creating and defining tags, and training models, all of which users can do through a simple user interface. As per the data type, customized templates can be chosen for visualizing actionable insights through dashboards and pre-made text analysis models.

How to use this Text Analysis Software?

• With the help of Zapier or APIs, direct integrations with the applications can be done for uploading the data from CSV or Excel sheets.
• Leveraging text analysis models, the text can be tagged automatically.
• By connecting with visualization tools such as Tableau, Looker, or Google Data Studio, insights of the data can be seen.

Features:

• Data Visualization: Custom charts and visualizations can be created using multiple data inputs.
• Pre-Built and Custom Learning Model: Leveraging machine learning, the models can be trained using custom tags and datasets.
• Customized Templates: As per the data, choose the template that best matches your preferences. Leveraging API, SQL connection, and native integrations, the applications can be connected with the BI tools.

What do we like about this tool?

For overall increasing the profit of an organization and for enhancing the experience of customers, MonkeyLearn deeply analyzed the data.

In a few minutes, you can extract insights from a large chunk of data. For streamlining the workflows, increasing productivity, and for overall smoothing the user onboarding process, MonkeyLearn is one of the top-notch text analysis software preferred by users.

Pros:

• It is easy to implement with an all-in-one solution.
• More simply, the texts are classified into labels.
• Tracking the customer feedback is easy. Analyzing customer feedback by gathering data is easy to do.
• Classifying and tagging the text for building and training the machine learning model can be easily done.

Cons:

• For the SME companies, it is highly expensive.
• The more you utilize third-party integration tools, the higher the subscription price becomes.
• With the external data sources, limited integrations are only supportable.

#2) IBM Watson

Best for training, validating, and tuning machine learning models.

Leveraging Watson Personality Insights, Watson Tone Analyzer, and IBM Watson Natural Language tools, understanding and classifying the entities or extracting the keywords from text, can be done.

The IBM Watson classifier classifies meaningful information according to the categories. After that customized machine learning models are trained for understanding the languages and predicting the text accuracy.

Concepts, emotions, sentiment, relations, metadata, and semantic roles can be extracted from the documents, content, images, and other publications.

How to use the tool?

• IBM is a data analytics processor. It uses natural language processing to analyze human speech for meaning and syntax.
• On the vast repository of data, analytics is performed.
• After that, the processing of human-posed questions is answered. It usually takes a fraction of a second to process.

Features:

• Watson Orchestrate: Using the automated APIs and RPA integrations, for complex or repetitive tasks, the process can be streamlined.
• Watson Assistant: Automated answers with accuracy and efficiency across the apps, devices, and channels can be done through the IBM Watson Assistant functionality feature.
• Watson Discovery: The natural AI models help fetch information from complicated business documents.
• Developers can write Watson Code Assistant: Code with AI-generated recommendations with the help of IBM Watson Code Assistant.

What do we like in this tool?

IBM Watson is an ideal tool as it is a combination of artificial intelligence and sophisticated analytical software for giving optimal performance. You can automate the entire AI lifecycle on an open multi-cloud architecture. IBM Watson Studio is one of the most preferred AI development platforms by the data scientists and developers.

Pros:

• Accurate and reliable tool. The customers can use it to analyze customer needs and preferences. IBM Watson will give trustworthy results that can be leveraged for future use.
• With the ease of using APIs, IBM Watson can easily integrate with any environment.
• Extracting important concepts, keywords and sentiment analysis can be done very easily.
• Reviewers found IBM Watson user-friendly, ease-to-use, accessible, and consistent.

Cons:

• Integration with foreign languages is a difficult process.
• To get accurate results, a lot of data scrubbing and teaching are required.
• A lot of tweaking is required to streamline the work. The data is all in the form of clumsy.

#3) Voyant

Best for web-based reading and analysis tool for digital text.

It is an open-source, web-based application used for text analysis. For analyzing the online texts or documents uploaded by users, it is the best tool.

For searching frequently used words or phrases, frequency distribution plots, and keyword searching in context analysis, it is one of the best and most preferred tools. A variety of text formats, such as HTML, TXT, PDF, RTF, XML, and MS Word documents, can be uploaded for analysis.

With the powerful functionality of user-friendliness, no installation process or login credentials are required to access the Voyant tool.

How to Use Voyant for Text Analysis: In this video, how to leverage the Voyant tool is explained in a detailed way:

Features:

• Easy to Use and Integrate: Voyant integrates with collaborative research processes. Sharing corpora and embedding tools into web pages is also possible.
• Powerful: It is a tool that can be used for exploration and for assisting with interpretative practices. Some compelling interpretations can be constructed while using the tool.
• Multiple Languages: Voyant tool can work with any language. But, it does not provide language-specific functionality.

What do we like about this tool?

The visualizations created through the Voyant tool are exportable. For adding any functionality to the project, blogs, essays, collections, and many more, it is a perfect tool. For analyzing a large chunk of data through a wide number of tools, it is the best text analysis software tool.

Pros:

• In several ways, the text can be uploaded to the Voyant tool. You have the option to paste the text directly, paste URLs, or upload files.
• Any technical skills like text mining, topic modeling, or any other activities are not required to learn. Voyant is an easy tool to use for text analysis.

Cons:

• Basic interface includes five panes, i.e. Reader, Cirrus, Trends, Summary, and Contexts. All the panes are interactive. Therefore, selection in one pane may affect the display in another pane.
• Gathering information using visualization tools like Lava, Knot, and Mandala is difficult and time-consuming. Prolonged text loading time is also a challenge and one of the biggest challenges of the Voyant tool.

#4) Google Cloud NLP

Best for real time analysis of insights from the unstructured data.

For syntax analysis, content classification, entity extraction, and sentiment analysis, the Google Cloud natural language processing platform is the best one to use. From the unstructured text, documents, etc. meaning insights can be extracted through Google Cloud NLP.

Users can train the machine learning models used for natural language processing using topics, domain keywords, and sentiments.

How to use the tool?

• Leveraging machine learning the structure, text, and meaning of text can be known.
• The information about the important texts can be extracted from texts and speech.
• Training customized machine learning models for better accuracy and results.
• With the help of natural language API, natural language understanding can be applied to the applications.

Features:

• Content Targeting and Discovery: Using Google’s state-of-the-art language, the content across media can be classified. Therefore, the content can easily reach the target audience.
• Insights from Customers: Leveraging entity analysis, label fields within a document over an email, chat, and social media can be fetched.
• Multimedia and Multilingual Support: With the help of speech-to-text API, insights from audio can be taken out. Google NLP supports most languages.

What do we like about this tool?

Across many languages, domains, and scales, the Google natural language processing algorithm can be applied. Overall, the Google Cloud NLP has enhanced the experience of users in reading, searching, and translating data through mobile applications or websites. It can easily measure the sentiment and important parts of a text.

Pros:

• It supports a wide number of platforms.
• The models can be easily trained.
• Ease of integration with Google BigQuery and Google PubSub makes easy-to-use ready-made pipelines.

Cons:

• The security feature is not too good. Unauthorized users can easily access the data.
• For customizing the existing modules and libraries, a lot of time and experience is needed.
• Up-to-date documentation is not there for many supporting AI modules. It results in a lot of time consumption for debugging.

Free Trial: Google offers a 90-day free trial period for 300 dollars. It includes free credits to use any Google Cloud product. Get your hands on over 20 Google Cloud products for free during the trial period.

Ratings: 4.5 out of 5 is the rating for Google NLP cloud.

#5) Thematic

Best for actionable intelligence from unstructured data for getting specific insights.

Leveraging artificial intelligence tools like thematic intelligence, thematic insights, and thematic catalyst extraction of text, trending patterns, themes, and data visualization can be done easily.

For qualitative analysis of data, the thematic analysis process involves reading of data, finding patterns and selecting themes as per the dataset. Thematic offers powerful tools for analyzing a wide variety of data types, such as audio, video, websites, tweets, survey responses, and many more.

Leveraging a wide variety of visual tools, qualitative research, and analysis can be done. The main plus point of using Thematic text analytic tool is there is no need for any supervision.

Features:

• Tag and Theme: Easily modify the themes with edit, ignore, and merge functionality features of thematic.
• Connect and Combine: With one-click integrations, redact the data in a compliant and safe way through the connect and combine function.
• Slice and Dice: Analyze the customer feedback through the feedback filter like date, sentiment, product, region, etc. Following it, the Thematic slice and dice functionality send automatic personalized messages to the customer, support issues, and operations team.

What do we like about this tool?

Leveraging Thematic text analysis software is very easy to save time. It delivers accurate results that are realistic and trustworthy. Thematic text analytics can help eliminate human errors.

As per the needs of the business, Thematic text analytics is customizable at any level. With the help of powerful AI, you can add a personalized touch to visualize any dataset.

Pros:

• With a flexible approach, the data can be modified. In different formats, the data can be collected.
• Through Thematic text analysis, a large amount of data can be handled very easily. The data is divided into different datasets for qualitative analysis.
• Without any preconceptions, the data can be dug for analysis. The data can generate real codes. Overall, Thematic analysis increases the authenticity of the analysis.

Cons:

• The coding system is open to interpretation and it is subjective. Different researchers come to different conclusions when analyzing the data and coding. It leads to unreliable results and inaccurate results.
• Different types of data are generated from distinct themes. The researchers find it difficult to distinguish between themes and codes.

#6) Amazon Comprehend

Best for understanding and deriving valuable insights from the texts.

Leveraging Amazon Comprehend pre-trained natural language processing, the models can be customized and trained as per your own rules. The texts can be evaluated and sorted according to the sentiments, topics, and based on other things.

The models are continuously trained with the help of new data gathered from distinct sources and categories. For small workloads, real-time analysis can be done while for handling large sets of documents, asynchronous jobs need to be run. Amazon Comprehend can easily be integrated with AWS Lambda, Amazon S3, and AWS KMS.

AWS Identity and Access Management (IAM) is supportable for securely controlling the access of Amazon Comprehend operations.

How to use this tool?

• For gathering the insights of a set of documents, a pre-trained model is required. Leveraging a large body of text, the model is continuously trained.
• Customized models are built up for custom entity recognition and custom classification. With the help of Flywheels, the custom models can be managed.
• The researchers utilize topic modeling to examine a corpus of documents. Based on similar keywords, the documents are organized.
• There are two document processing modes, i.e. asynchronous and synchronous. With synchronous mode, you can process a batch of 25 documents at once. While, in asynchronous mode, many documents can be processed.
• For safety and security reasons of data, AWS key management service is used for enhancing the encryption of data.

Features:

• Custom Classification: With the help of custom classification APIs, the text classification model can be classified into different categories.
• Sentiment Analysis: Overall sentiment through the text can be gathered through sentiment analysis features. The sentiment can be positive, negative, or neutral.
• Keyphrase Extraction: The keyphrase APIs extract the key points from a text or document along with a confidence score.

What do we like about this tool?

Using Amazon Comprehend, natural language processing can be integrated powerfully with the applications. Leveraging a simple API, the complexity of building text analytics capabilities can be completely removed.

With the help of NLP algorithms, sentiments, keyphrases and entities can be automatically extracted and analyzed.

Pros:

• The text classification models can be customized as per the set of entities needed as per organization demand.
• Identification of language and extracting key phrases, places, events, location is highly easy with Amazon Comprehend.
• Pay as per the use is an excellent thing for Amazon Comprehend text analytics.

Cons:

• Specifically for litigation, text-related documents or for extracting insights, if Amazon Comprehend could have provided support, then it would have been good.
• Integration with the REST API is not easy using Amazon Comprehend.
• For REST APIs supporting Java SDK, if developers can do the testing without creating any account or credentials would be a big advantage.

#7) Aylien

Best for identifying and visualizing trends with advanced analytics features.

For gaining insights from the texts, Aylien used machine learning, natural language processing and artificial intelligence like advanced technologies. By leveraging all these tools, Aylien can extract valuable texts from a large set of documents.

For the overall development of a brand, creating strategies and customer support, the analysis of text is highly important. Leveraging it, the mindset of a consumer can be read or trends of the market can be predicted.

Instant access to 80,000 sources, 5.6M recognized entities, 4500 industry tags and categories are the unique things provided by the Aylien software tool. The newly generated data can be integrated with the apps and models for creating reports.

How to use the tool?

• Building the Corpus: Using the Tweepy API for gathering the sample text data.
• Analyzing Text: With the help of SDK, the piece of text can be analyzed. You can analyze the sentiment through that text.
• Visualizing Results: Using Python Pandas and Matplotlib the results of work can be visualized.

What do we like in this tool?

The SDKs of the Aylien software tool is available in seven major programming languages. Coding is not necessary for creating a custom analytic framework while using the Aylien software.

Being a cloud-based business intelligence solution, the natural language processing (NLP) models can be built and deployed using it very easily.

Features:

• Issue Tracking: With advanced text analytics, the critical insights of data can be identified and visualized.
• Data Analysis: The developers can build datasets from sampled text, knowledge base, and labeled data.
• Model Maintenance: The performance of a model can be evaluated. Through continuous iterations, the model can be made more precise and accurate.

Pros:

• Quick support from customer care.
• The documentation is well-written and can be used. Documentation of APIs is also understandable and easy to implement.

Cons:

• Many times, the APIs do not respond and are not openable.
• Fetching the right story and distribution of the folder is not so good.

#8) Chattermill

Best for unifying the customer data and receiving actionable intelligence.

Leveraging Chattermill, the customer data can be made unified. Using artificial intelligence, analyzing the data on a large scale can be done. For understanding the voice of customers, Chattermill tool provides deep intelligence.

Through the gathered data of the customer, customer feedback analytics, support data analytics, product feedback analytics, and social CX analytics can be done. It overall helps in identifying the interests of customers, their likes, beliefs, buying and watching habits for a product or service.

For managing the risk and analyzing the potential of a customer, it is the best tool for financial institutions.

Features:

• Security: Chattermill provides complete safety and security to the customer database. Constant monitoring, inspection, and auditing are done to keep the system up-to-date.
• Analysis: From name recognition, keyphrase extraction, topic analysis, sentiment analysis, and language identification, Chattermill automates every single technical process.
• Setup and Installation: In multiple formats of data and from distinct sources, the data can be imported. Without doing any coding, the data can be analyzed.
• Process Management: Chattermill does real-time analysis, triggers alerts and in real-time action quickly resolves the issues.

What do we like in this tool?

For the enterprise brand, startups, and scaleups it is the best tool to use for text analytics. With proactive alerting, collaborative transparency, intuitive user experience, unification of data sources and use cases and for getting contextually rich insights it is one of the most liked tools by enterprises.

Pros:

• More than 50+ integrations can be done to unify the customer data.
• Actionable insights can be gained through this platform. It assists in making informed and calculative decisions.
• Specifically for CX, the Chattermill AI model is built.

Cons:

• It is difficult to remove the existing users from the service as well as categories.
• Prompt and timely response from the technical support team is required.
• The generated reports are not automatically saved when navigating to distinct pages.
• Many customization options available lead to confusion.

#9) Microsoft Azure

Best for extracting, classifying and understanding text within documents.

Leveraging Microsoft Azure, evaluating the text in a wide range of languages can be done without learning any machine learning. Using cognitive service for language features, a deeper understanding of customer opinions and sentiments can be done.

For all types of unstructured and semi-structured content such as URLs, FAQs, support documents, blogs, product manuals, and many more a conversational layer is created over the data by the Microsoft Azure text analytics tool.

Language detection, sentiment analysis, opinion mining, summarization, key phrase extraction, entity linking, custom text classification, conversational language understanding, orchestration workflow, and question answering are some of the key features of this Microsoft Azure software tool.

Features:

• By using custom text classification, one can automatically classify unstructured texts and documents based on domain-specific labels.
• From a broad range of pre-built entities, personally identifiable information (PII) can be identified and categorized. This feature is known as entity recognition.
• Leveraging opinion mining, the customer perception about a particular product or service can be known through the texts and tweets gathered from social media platforms like reviews, ratings, and other things.

What do we like about this tool?

Azure ML is a cloud-computing tool. It is a high-speed and reliable tool, as none of the processing is made through the computer. By just creating a few blocks and connecting a few lines, text analytics can be done. With the help of its visual interface, charts can be created for quickly checking the performance of any customized model.

Pros:

• Centralized platform for machine learning life-cycle.
• An effective and easy way to deploy the models as a web service.
• The IDs can be tracked and results can be derived from it.
• Most of the companies have the data set in CSV, and Excel sheets. Therefore, connecting Microsoft Azure with the Microsoft Suite is easy.

Cons:

• It is very difficult to apply and run the Python codes.
• Documentation as per the use-case and scenario, is not available. Proper documentation is needed to understand the things.
• It is highly costly, and it is difficult to integrate the data with the models.

#10) SAS Visual Text Analytics

Best for Analytics, Artificial Intelligence, and Data Management.

For creating and sharing reports with clients, this SAS visual text analytics tool provides a dashboard for monitoring business performance. SAS provides an integrated and modern environment for governed discovery and exploration.

Managing the data, model development, and deploying it are the primary steps of SAS Visual Text Analytics too. Leveraging machine learning, deep learning, natural language processing, and linguistic rules, the right information is extracted to make the right decision through SAS visual text analytics.

With the help of embedded visualization capabilities, intuitive dashboards can be created as per the data and analytics. On average, the SAS visual text analysis software is 30 times faster in executing the tasks. Using this tool, 90% of the operating cost can also be cut down.

Features:

• Parsing: For analyzing the text at a large scale, the text is separated into words, punctuation marks, phrases, and other elements through a parsing feature.
• Flexible Deployment: Easy deployment of a model through API, Hadoop, as batch or stream can be done. By taking maximum advantage of computing resources, the latency time while delivering the results can also be reduced.
• Corpus Analysis: For data cleansing, sampling effectively, and separating the noise the models leverage natural language generation (NLG). This methodology is known as corpus analysis.

What do we like in this tool?

For displaying emerging trends, spotting opportunities for action, and unlocking the value of unstructured text data, the SAS Visual text-analytics software tool is preferred. It has an easy-to-use interface for dashboarding with interactive data exploration features.

Pros:

• Sharing the data is very easy and hassle-free.
• The security of data is very good and the visualization provided by the tool also has a good layout and design.
• User-friendly tools with drag-and-drop functionality.
• With the help of a high-speed processor, millions of records can be processed. Integration with most of the data sources is possible with SAS Visual.

Cons:

• There is a need for tutorials for beginners to understand the workings of the SAS Visual Analytics tool.
• With the non-SAS tools, it is very difficult to integrate.
• Online support at the moment is not available as it is not an open-source tool.
• From Tableau and Power BI software tools, the features for creating the dashboard can be taken.

Text Analyzer – FAQs

Conclusion

To progress the growth of the business, increase sales, and expansion of customers there is a need to understand the customers. Hence, for understanding market trends, customer voice data plays an important role. By collecting data from feedback, surveys, websites, and other sources, we can understand the sentiment of customers.

Data alone cannot do anything. A team of scientists and data analysts is required to fetch valuable information from all the data sources. After gaining useful insights, the upcoming market trends can be predicted. Thus, we can say that Text Analysis Software and Text Analytics play a big role in today’s market.

Research Process:

• We have spent more than 20 hours researching and reviewing all these text analytics tools. After reading this informative article, users can quickly select the text analytics tool as per their requirements.
• Total Text Analysis Software researched online: 36
• Top Text Mining Tools shortlisted for review: 10