The top 7 best text analysis software

1. Voxco

Voxco is an omnichannel insights tool. Our robust text analytics software can instantly analyze millions of lines of open text to help you understand the actual attitudes of your customers. The user-friendly layout of our text analytics software makes it simple to use and navigate, giving you a hassle-free and seamless experience.

The best text analysis software from Voxco intelligence makes use of password management, robust security measures, and granular access permissions to guarantee that the data of your customers is managed with the utmost care and kept private.

With the help of Voxco’s AI-enabled text analytics software, you can instantly evaluate millions of texts to better understand your customers’ needs.

Automatic Language Recognition 

To analyze customer feedback quicker, interpret text from the data and convert it to machine language.

For quicker consumer feedback analysis, software for text mining interprets text from the data and converts it to machine language.

2. Monkey Learn

A machine learning tool called MonkeyLearn automatically examines text and draws useful conclusions from data. For greater levels of accuracy, you can use pre-trained best text analysis software or make your own by customizing them to your requirements.

You can conduct sentiment analysis, keyword extraction, intent categorization, and language detection using text analysis models, which include text classifiers and text extractors.

With their own WordCloudGenerator, MonkeyLearn welcomes word clouds.

MonkeyLearn automatically tags your content using pre-built text analysis models. You have the option of choosing pre-built models or creating your unique classifications and extractors.

Any data can be used to immediately create a word cloud. It relies on how many categories you are after when it comes to categorizing data.

You can get direct access to your data through important integrations with common applications like Excel, Google Sheets, Zapier, RapidMiner, and Zendesk. It is necessary to pre-clean data before uploading it.

Also read : sentiment analysis tool

3. Qualtrics XM Discover

To evaluate unstructured text data, XM Discover uses more than 130 pre-built industry templates and categorization models. Customers’ motivations, emotions, and next steps are revealed by XM Discover.

Your customer service chatbots can be enhanced using XM Discover analytics to better comprehend people. It integrates with other Qualtrics products to provide an all-in-one system that covers data collection, analysis, and implementation.

A wide range of preset models for various industries is available in XM Discover, which employs both rule-based and machine-learning techniques. Specific components can be customized by users to better represent their businesses.

How much time does it take to launch?

It will take a few weeks to get going if your dataset closely fits one of the taxonomies that XM Discover provides. However, a highly customized execution could take up to six months to complete. This usually entails an XM Discover expert working closely with the business stakeholders to develop complex rules to capture topics of interest.

With more than 100 pre-built connectors for different data integrations, XM Discover connects to a variety of feedback and interaction sources. These include chat, social media, surveys, and contact center records.

4.  Chattermill

Chattermill is a platform for unified customer feedback analytics that unifies, measures, and examines your customer feedback. The platform uses theme and sentiment AI to help you see how customers feel, stay on top of new topics, and understand what keeps them coming back.

To find the hidden layers of meaning in your data and determine what influences user experience, use Chattermill’s filters. To correctly find and forecast opportunities to increase revenue, brand loyalty, and customer retention across customer touchpoints, analyze the signals in your customers’ feedback.

The predefined taxonomies used by Chattermill are centered on typical customer success problems. If necessary, their data staff will adapt the model to your data.

Chattermill accepts input from a variety of sources. It has several pre-configured interfaces and an API for building bespoke ones.

How much time does it take to launch?

It takes at least two weeks to develop the original theme structure, followed by another two weeks to review and improve it.

5. Lexalytics

A conventional text analysis program called Lexalytics converts complicated text documents into data and insights. Intentions, themes, entities, categories, subjects, and multi-layered sentiment scores are all extracted by Lexalytics using NLP. You can see what’s being discussed, what individuals are feeling, and why.

The product consists of three major components: 

• Salience, their main text analytics and NLP software library designed for data scientists.
• Salience’s powers are integrated into the current data analytics infrastructure by Semantria.
• Built on the strength of the Semantria API is Spotlight, the comprehensive Lexalytics solution. Interactive dashboards allow you to share your findings and visualize your outcomes.

For composing summaries, extracting keyphrases (which they refer to as themes), entities (proper nouns), and classifying documents by subjects, Lexalytics has pre-built models available. (e.g. Sports). The user has the option to customize these models, which are available in packs for particular sectors.

Lexalytics can handle text in more than 20 different languages and supports a broad range of data sources.

How much time does it take to launch?

Initial results are available right away, but customization may take weeks if your business is not included in one of the available packs.

6. IBM Watson

Technology behemoth IBM provides a set of AI tools that extract and categorize data from organized or unstructured text data. The IBM Watson Tone Analyzer, Watson Personality Insights, and IBM Watson Natural Language Understanding & Classifier are all helpful tools.

In addition to other things, IBM Watson Natural Language Understanding extracts ideas, entities, keywords, and categories. When used for sentiment analysis, it not only divides the text into the three general categories of positive, negative, and neutral sentiments but also divides these sentiments into specific emotional categories like perplexed, excited, sad, confident, etc.

Developers can assign a classification to text using the IBM Watson Natural Language Classifier without having to be experts in statistical or machine learning algorithms. By uploading their data, developers can build a custom machine-learning model that will classify texts, extract insights, and spot patterns.

7. Google Cloud NLP

Not one to fall behind, Google offers Google Cloud NLP as a response to the expanding text analysis trend. This tool aids companies in extracting insights from unstructured text and giving it meaning.

The text analysis applications that Google Cloud NLP works on include entity extraction, syntax analysis, sentiment analysis, and content classification. All you need to fine-tune your models to your domain-specific keywords, sentiments, subjects, etc., are some training data if you’re eager to train your machine-learning models.

One more benefit! You can combine your Cloud Storage with Google’s AI tools to provide a smooth text analysis experience.