Text analytics Solution

Social Media Analysis

The explosion of social media has made available a gold mine of unstructured data rich with personal views and opinions. They are extremely valuable for many social media analysis applications. The challenge is how to derive actionable insights from massive amounts of unstructured data.

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Voice of the Customer

Customers often share their opinions and experience with a brand on social media. That makes social media a treasure trove of information to measure and monitor customer satisfaction, perform trend analysis, and get alerts about any poor customer experience so that it can be corrected promptly. The goals are to improve customer experience and retention and to protect a brand’s reputation.

NetOwl’s advanced sentiment analysis goes beyond basic positive and negative sentiments to provide a finer-grained sentiment ontology that captures a broad set of concepts such as intent and behaviors (e.g., threats to boycott a brand, product recommendations).

Public Opinion and Geopolitical Monitoring

Social media contains a wealth of unstructured data of great value to organizations monitoring, analyzing, and forecasting public opinion and geopolitical events. For instance, it is of great interest to policy analysts, law enforcement officers, or emergency responders to gauge and monitor public support for political leaders, election candidates, and policies as well as the public response to unfolding disasters and conflicts such as military campaigns and social uprising.

NetOwl’s sentiment analysis and event extraction offer a unique capability to capture public sentiment and adverse events across the world geospatially in real time.

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Market Research

Social media contains highly valuable information about customer preferences, wants, and needs. These insights are critical to inform product design for competitive advantage and revenue growth.

NetOwl’s advanced entity- and aspect-based sentiment analysis not only identifies sentiments but also pinpoints the specific objects and aspects of those sentiments. For any entity of interest (e.g., vacuum cleaners), the user can see the breakdown of consumer opinion on the specific aspects that those sentiments are about (e.g., price, battery, suction) so that specific issues are surfaced for concrete actions. NetOwl normalizes the phrases that express the identified objects, aspects, and sentiments so that they can be aggregated and quantified to provide a more accurate picture