Convert Your Text into Insights With AI

The Growing Challenges
The common bottlenecks which might slow down your business growth.
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Sentiment Analysis
Unable to understand the vital signs and sentiments from the given text to predict accurate results.
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Real-time Analysis
Inability to carry out real-time text analytics to solve the complex challenges instantly.
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Limited pre-defined analytics models that cannot be customized as per the changing user requirements.
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Language Barriers
Inability to detect the language of the text to employ text analytics AI with accuracy.
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Prediction Accuracy
Low accuracy of predicting the language, text summary, keyword extraction from the given text.
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Text Mining
Lack of potential to offer personalized autonomous mining and proper knowledge management systems.
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How OSP works

Text Analytics AI Process to Understand Your Data

AI in Text Analytics to reveal key information and actionable data from your text.



  • Language Identification

    In today’s era of the global economy and smart communication channels, language barriers prove to be a challenge. Identifying a language, understanding and sending an automated reply through AI-based chatbots is what businesses are looking for. OSP’ AI in text analytics promises to offer a language detection module that analyzes text to identify the language of the text instantly. Language identification in text analytics AI has the potential to categorize content and enhance the search results especially for multilingual documents. Our artificial intelligence text analytics can help you to identify the lanaguage of the text from social media, image captions, email subject lines, news headlines, keywords, queries, tweets, metadata, files, logs, and more.

  • Information Extraction

    Information extraction from the given text is one of the most valuable and advanced feature offered by our text analytics AI. It identifies key phrases and relationships within the text. OSP’ artificial intelligence text analytics does this by looking for predefined sequences in a text, a process usually called pattern matching, typically based on regular expressions. The named entity recognition (NER) seeks to locate and classify atomic elements in text into predefined categories that usually match with pre-established ontologies. NER techniques extract features such as the names of persons, organizations, places, quantities, monetary values, temporal or spatial expressions, stock values, percentages, protein or gene names, etc.

  • Text Summarization

    Text summarization offered by the artificial intelligence text analytics falls under the natural language generation. It helps in figuring out whether or not a lengthy document meets the user’s requirements and is worth reading for further information. With comprehensive texts, text analytics AI software solutions can process and summarize the document in the time it would take the user to read the first paragraph. The key to summarization is to decrease the length and detail of a document while retaining its main points and overall meaning. The text analytics AI solutions statistically weigh and rank important sentences from an article. Summarization tool can be customized to search for headings and other markers of subtopics in order to identify the key points of a document. OSP’s text analytics AI solutions offers powerful text summarization that can be classified into two broad


    • Shallow analysis: It is restricted to the syntactic level of representation and try to extract significant parts of the text.
    • Deeper analysis: It assumes a semantics level of representation of the original text.
  • Clustering

    Grouping similar documents together are called clustering. For clustering, understanding these documents is a vital step. It differs from the classification as the classification offered text analytics AI is based on the supervises machine learning. Classification requires previous knowledge to assign a given document to a presented category. Clustering module in text analytics AI software is based on unsupervised learning: there are no previously defined topics or categories. Using clustering, documents can appear in various subtopics, thus ensuring that a valuable document will not be omitted from search results. A basic clustering algorithm in our text analytics AI solutions creates a vector of topics for each document and assigns the document to a given topic cluster. Medicine and Legal research papers have been a prolific ground to apply text clustering techniques offered by text analytics AI.


  • Concept Linkage

    OSP’s text analytics AI has inbuilt concept linkage tools that can connect related documents by recognizing their commonly-shared concepts. The artificial analytics text analytics can also help users find the data that they perhaps would not have discovered using traditional searching methods. It encourages browsing for information rather than searching for it. In the fields like legal and biomedical, where there is a huge amount of research is done and a pile of textual data seems overwhelming. Pir tailored text analytics AI software has concept linkage –  a valuable concept in text mining. OSP’s text analytics AI has the search tool for finding related and possibly unfamiliar concepts that lie on a path between two well-known concepts. The tool searches semistructured data in knowledge repositories which are based on finding previously unknown concepts.

  • Entity Linking

    Entities like the people, organizations, places, and things are the key players in your text data. Using a synthesis of established text analysis best practices and machine learning statistical modeling, our text analytics AI solutions uncover these entities. Our text analytics AI can deliver the structure, clarity, and insight to your content. OSP can customize the text analytics AI software solutions for you to resolve entity ambiguity by finding the synonyms and connecting information about an entity within and across documents. Our artificial intelligence text analytics has the feature to add metadata from the linking process to your text. It provides the accuracy and prediction confidence measures to boost the entity-centric search and notification. Our text analytics AI can track new entities in text streams, and create custom knowledge graphs.

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Use Cases

What We Offer?

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Text Concept Extraction

Our text analytics AI has the potential of understanding the gist of the given text with the text concept extraction module. The text analytics AI focuses on the central keywords in given documents or databases to identify the 'keyphrases' and 'concepts' for the provided input. These smart inputs are based on the frequency and linguistic patterns observed in the text, ranking them according to their relative value. Grasping the key phrases and concepts in each text enables users to automatically tag, segregate, and organize their data.
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Sentiment Analysis

OSP provides tailored artificial intelligence text analysis that includes next-gen sentiment analysis. Our sentiment analysis module reads, understands and extracts the attitudes, opinions, and emotions from the given text. Our AI in text analytics uses natural language processing and computational linguistics to deliver sentiment scores for entire documents, or for individual entities within a larger body of text. Sentiment analysis helps to detect positive and negative sentiment in customer reviews, social media, and discussion forums.
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Text Classification

OSP' builds custom text analytics AI software that incorporates advanced text classification based on pre-defined rules and algorithms. Our AI in text analytics leverage rule-based text classification to automatically assign sentiments or topics to any given text it processes. With the advanced text classification in AI text analytics, you can define your own categories and classify your documents in seconds. Classifying and tagging content and product proves beneficial for E-commerce, news agencies, content curators, blogs, directories, etc.
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