One of the application of automatic ideology analysis in my PhD thesis work is to predict the ideological perspective from which an article is written. I make a web-based demo, Ideology-O-Meter, that takes a input text on the Israeli-Palestinian conflict and analyze how likely the text is written from the Israeli or the Palestinian perspective.
There are three panels in the Ideology-O-Meter demo. In the left panel, you can type any text you would like to identify its ideological perspective on the Israeli-Palestinian conflict. I prepare example texts written by real Israeli and Palestinian authors on the bitterlemons.org. You can use these examples by clicking one of the two buttons above the text box. After you press the Identify button at the bottom, the input text will be sent to the automatic ideology analysis program running in the background. The program will parse the text, and infer the likelihood of expressing ideological beliefs using the Joint Topic and Perspective Model.
The results are shown in the middle and right panels. The middle panel is the Ideology-O-Meter, and the position of arrow indicates how strongly the input text conveys one of the two ideological perspectives. The more extremely a text expresses the Israeli view, the more the arrow moves to the right. Similarly, the more extremely a text expresses the Palestinian view, the more the arrow moves to the left. In the above example, the text appears to be written very much from the Palestinian perspective.
The third panel lists the top 10 more frequent words in the input text and their frequencies (in dark yellow). The longer the bar, the more frequently the word appears in the input text. The light yellow bar is the expected frequency in articles written from the Palestinian perspective that the Joint Topic and Perspective Model learns from the bitterlemons corpus. The closer the two bars in proportion, the more likely the input text is written from the Palestinian perspective.
You can read more about the statisitcal model behind the scene in our coming ECML paper.