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The Critical Value of Sentiment Analysis for Geopolitical Monitoring
Many companies and organizations rely on open source data such as news and social media to monitor, analyze, and forecast an array of geopolitical events. For instance, they use open source data to gauge public support for political leaders and policies and to monitor public response to conflicts such as military campaigns or social uprising.
However, with staggering and fast growing volumes of unstructured open source data, it is impractical to analyze open source information manually. Instead, automated processes are necessary to transform unstructured data into usable structured data that can render actionable insights.
Sentiment Analysis: A Critical Technology for Geopolitical Monitoring
Sentiment Analysis software is able to identify sentiments such as likes, dislikes, opinions, and intent. Sentiment Analysis software aids organizations and individuals by converting unstructured data into semantically labelled structured data that can then be easily analyzed to discover new trends and monitor evolving situations.
However, to analyze public opinion, it is not sufficient to identify positive and negative sentiments. For Sentiment Analysis to be truly useful, it must also be able to identify the objects of those sentiments (e.g., a foreign political leader, a political party) and the specific aspect being discussed (e.g., the character of a leader, the new policy of a government).
NetOwl’s Sentiment Analysis
NetOwl’s advanced Sentiment Analysis software is able to process large amounts of unstructured data in near-real time. Using Entity- and Aspect-based Sentiment Analysis, NetOwl detects not only the expressions of sentiment themselves, but also the targets of those sentiments. Is a social issue becoming a hot topic? Is the prime minister of a country of interest losing public support? What’s the public response to a military campaign?
Additionally, NetOwl normalizes entities and sentiments in an intelligent fashion to be able to aggregate and quantify sentiment data. Once the sentiment data has been normalized, aggregated, and quantified, the larger picture from a large collection of content can emerge and the sentiment data is amenable to advanced analytics such as predictive analytics.
NetOwl’s Sentiment Analysis dashboard provides multiple interactive charts to slice and dice data as desired. For any entity of interest (e.g., a prime minister), the user can see the breakdown of public opinion, the top positive and negative sentiments, the specific aspects that are the object of those sentiments (e.g., a prime minister’s character, his/her track record, a stance on a specific issue, a speech), and drill down to the source text for inspection and further analysis. Other useful charts show sentiment evolution over time. Furthermore, using location metadata and automated geotagging of mentioned place entities, sentiments can be plotted on a map to show, for instance, hot spots of support or disapproval.
NetOwl’s sentiment analysis is valuable to anyone who would like real-time monitoring of geopolitical events and changing attitudes toward them.