NetOwl’s sentiment analysis software goes far beyond traditional sentiment analysis where “positive” or “negative” sentiment is assigned at the document or sentence level.  NetOwl recognizes the multiple, sometimes conflicting, sentiments about entities that may exist within a single document.
Entity- and Aspect-based Sentiment Analysis

Entity- and Aspect-based Sentiment Analysis

NetOwl offers entity-based sentiment analysis as well as aspect-based (or feature-based) sentiment analysis. At the entity level, NetOwl identifies sentiments toward various types of entities such as people, organizations, brands, and products. At the aspect level, NetOwl captures the specific entity aspects that sentiments are about, for example, the price of a product, the new policy of a country, the campaign of a presidential candidate, etc.

By being able to pinpoint what the sentiment is about, NetOwl’s sentiment analysis is much more informative and useful because it:

  • Allows much more detailed sentiment analysis on each entity
  • Captures multiple, conflicting sentiments expressed within a single document or sentence.
Refined Sentiment Ontology

Refined Sentiment Ontology

NetOwl’s sentiment analysis tool does not just give “negative” or “positive” binary answers, but offers a more refined sentiment ontology to distinguish different opinions, attitudes, intentions, and behaviors. For example, NetOwl’s fine-grained sentiment ontology makes a distinction between a complaint about a product feature and a threat to boycott a brand.

This kind of distinction will allow organizations to conduct a more in-depth sentiment analysis of opinions and actions. In addition, organizations are able to prioritize and tailor their responses in a much more targeted fashion.

Normalization for Trend and Predictive Analysis

Normalization for Trend and Predictive Analysis

NetOwl’s sentiment analysis software assigns normalized forms to extracted entities (both names and descriptive phrases) and sentiment expressions. It takes into account capitalization, acronyms, abbreviations, nicknames, morphological variants (e.g., number, tense), etc.

This normalization capability is critical to be able to classify, aggregate, and quantify the myriad sentiments expressed in a large data set. NetOwl allows production of much more accurate and meaningful graphs, charts, and dashboards for more effective sentiment analysis, discovery, and monitoring.

Sentiment Analysis Applications

Sentiment Analysis Applications

NetOwl’s sentiment analysis has been trained extensively on data from various social media platforms, including Twitter, Facebook, and blogs over many domains. It is useful for many applications, including CRM, online reputation monitoring, market research, online marketing, employee hiring and retention, online review monitoring, election campaigns, public opinion, and geopolitical monitoring.

Key Features of Sentiment Analysis

  • Accurate & Fast

    Accurate & Fast

    Provides state-of-the-art accuracy on many different types of text.  Fast and scalable for real-time analysis.

  • Extensive Entity Coverage

    Extensive Entity Coverage

    Handles all types of entities as targets of sentiment – named and non-named entities of major entity types and beyond.

  • Multilingual

    Multilingual

    Currently available in English and Arabic. Other languages are on the way.

  • On premise or In the Cloud

    On premise or In the Cloud

    Supports Docker and Kubernetes for easy and scalable deployment.

  • Easy Integration

    Easy Integration

    REST APIs offer easy integration.

  • Semantic Disambiguation

    Semantic Disambiguation

    Distinguishes semantic ambiguities such as “cool” in “cool wind” vs. “cool car” by utilizing linguistic context.

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