Bias Meter - How it Works
Biasly is a media bias rating agency that scans hundreds of articles daily to determine article, media, and politician sentiments and biases in the news. Our Bias proprietary algorithm (pending patent) uses deep neural network and natural language processing components that are trained to find entities in news articles and determine their political sentiments combined with machine learning of party policy sentiments to produce the Bias Score.
One may find themselves thinking, How can I trust data analysts and an A.I. machine learning system to tell me what is and isn’t biased? Biasly’s diverse staff (ranging on all sides of the aisle) decide from a nonpartisan point of view, sentence by sentence, whether a certain excerpt has sentiment leaning/bias in it, or if it is neutral.
Here at Biasly, we all serve a very essential purpose when it comes to political media. Alongside this, Biasly’s A.I. Bias Meter is a computer algorithm that learns and predicts biases in the media on its own, and unlike human beings, our computer algorithms are not subject to political bias.
Our Bias Meter is always improving to give users the most accurate, factual information regarding their news sources. Biasly’s ability to process and fetch news for you, in real time, with three different perspectives can provide insights about world events and political news, as well as improve media literacy within you and those who you associate with.
Feel free to learn about how these ratings are derived by viewing our Political Party Conservative and Liberal Stances
We have created the optimal news analysis system capable of determining political sentiment and conservative and liberal biases.
Our proprietary algorithm uses deep neural network and natural language processing components that are trained to find entities in news articles and determine their political sentiments combined with machine learning (artificial intelligence) of political party policy sentiments to produce the Bias Meter Score.
Biasly analyzes news articles to determine the following information: which policies are discussed in this article, which politicians are mentioned in this article, what is the overall sentiment toward specific policies, and what is the sentiment of an individual politician toward a specific policy.
Entities need not be exact. However, with a pre-defined list, noun chunks can be located with greater accuracy.
With this system, articles can be auto categorized and grouped together by political stance. Biasly makes it easy to find news coverage on specific people and topics.
Our technology was developed by PHD and Masters level data scientists who specialize in predictive analytics, Python, convolutional neural networks, natural language processing and more. A convolutional neural network is a type of deep neural network used for machine learning. Data enters the input and passes through connected layers where the data is transformed into the desired output data.
Biasly uses natural language processing and entity-specific sentiment analysis to transform the input data. Natural language processing is a means through which computers can process natural human language. Combined with entity-specific sentiment analysis, text can be rated as positive, negative, neutral, conservative, or liberal. Historically, natural language processing was done with manually written rules that delivered the desired output.
Today, language processing is done through statistical algorithms that learn the desired output by analyzing large amount of data that functions as examples of the desired output.