Sherpa.ai Content Analysis AI analyzes the content of text to extract valuable information

Sherpa.ai Content Analysis AI provides an easy solution for summarizing, interpreting, and extracting information.

  • Sentiment Analysis: Determine the sentiment load of a sentence, paragraph, or text. This AI model combines NLP and ML techniques to assign weighted sentiment scores to a text item. This way, a text item can be characterized by its overall emotional load (positive, neutral, or negative) and a combination of sentiment features (polarity, valence, arousal).
  • Latent Topics Extraction: The latent topics refer to groups of concepts extracted from a corpus of documents that conform to some abstract descriptions of high-level topics. Latent Topics Extraction aims to provide a short description of each text document as a distribution over topics, with each topic characterized as a distribution over words in a dictionary.
  • Trending Topic Identification: Some content has specific peaks of popularity during a short period of time. The Trending Topic model identifies texts that contain content related to a trending topic at a specific time and provides a score of the “trending” relevance.
  • Duplicated and Related Text Detection: Avoid duplicates and gather related texts. This AI model, based on Contextual Neural Language Models and ML, analyzes text documents at different levels of granularity to understand their individual context and detect cross-information. In doing so, the Duplicated and Related Text Detection detects whether two or more documents contain the same or related information.
  • Text Summarization: Don’t waste time analyzing text, let Sherpa.ai do it. This AI model, based on Contextual Neural Language Models and ML, analyzes text in order to provide extractive and abstractive summaries. It allows the extraction of a few sentences from a text that optimally summarize the whole of the text (extractive summary) and the automatic generation of a brief text (abstractive summary).
  • Fake News Detection: Fake News is one of the many ways that disinformation is spread, and is particularly prominent across digital media. New social technologies, notably Twitter, Facebook, and photo-sharing apps, facilitate rapid information-sharing and large-scale information, but can also spread misinformation, or information that is inaccurate or misleading. Fake News Detection predicts the chances that a particular news article is being intentionally deceptive.
  • Automatic News Categorization: News articles are usually related to one or more general types of information, e.g., economics, politics, international news, sports, culture, etc. Automatic News Categorization leverages ML models to learn to automatically categorize each news item into one or more of the available categories. That way, each news item is categorized according to its content.
  • Article Creation: This functionality allows the generation of news articles or reports about an event for which there exist structured data over time, e.g., sports chronicles. This model leverages the existence of structured data generated by one or more dynamic sources with neural language models, to create readable content.

Use Cases

Analyze Customer Opinions

Analyze customer interactions through customer support emails, social media posts, online comments, phone transcripts, etc., and discover which factors drive the most positive and negative experiences. This information can then be used to improve products and services, as well as to limit the spread of inaccurate and harmful information.

Display Article Sentiment

Publish indicators alongside news articles and opinion columns to give readers a clear understanding of the sentiment of content, even before they begin reading. This data strengthens customer relationships, by providing independent feedback on the material contained in each piece of writing and providing useful insight into the meaning behind the text.

Prevent the Spread of Fake News

Determine if content is intentionally deceptive and take action to prevent disinformation from spreading, in order to stop Fake News in its tracks. By predicting the likelihood that information is intentionally misleading or deceptive, it can be put a halt before it sweeps across the Internet.