News Analyzer

This Chrome extension is a powerful news analysis tool that allows users to evaluate, in an automated and visual way, the inclinations and trends of an article. It can check and visualize seven critical components of a news text, offering an in-depth analysis of the quality and bias of the information. These components are displayed graphically, as demonstrated in the attached image, where colored bars and circles indicate the ratings for each analyzed metric. The interface is user-friendly and provides direct visual feedback so users can quickly understand the main characteristics of the text.

Here is a detailed description of the seven analyses performed by the extension:

1. Polarization Analysis
This analysis aims to detect the degree of polarization present in the text. Polarization refers to the tendency of an article to align with a specific ideological perspective, whether it be left, right, or center. The extension’s algorithm checks for signs of extremism or moderation and classifies the content as follows:

- Highly polarized: The text strongly aligns with an extremist ideological view.
- Moderately polarized: The text shows noticeable but not extreme ideological leanings.
- Slightly polarized: The text displays mild ideological tendencies, remaining close to neutrality.
- Not polarized: The text shows no clear ideological leaning, staying neutral.

After classification, the extension justifies the analysis by offering examples from the content itself that support the diagnosis. For example, if the article uses terms like \"enemies of freedom\" or \"heroes of the nation\" to describe specific groups, it may be classified as highly polarized.

2. Emotional Tone
The emotional tone analyzes the predominant tone of the text, identifying whether it is positive, neutral, or negative. This analysis is crucial for understanding the emotional impact the article may have on the reader. The classification categories are:

- Positive: The article has an encouraging or optimistic tone.
- Slightly positive: The article presents mild positivity without exaggeration.
- Neutral: The text maintains a factual approach with no clear emotional charge.
- Slightly negative: There is a mild negativity or pessimism in the tone.
- Negative: The text is heavily loaded with negative emotions such as fear, anger, or sadness.

Here, the extension also justifies the choice, highlighting phrases or words that indicate the emotional tone, such as frequent use of alarming or reassuring terms.

3. Political Bias Analysis
In this part, the extension evaluates the political bias of the text, determining whether it reflects a left-wing, right-wing, or centrist viewpoint. The classifications are:

- Left: The text advocates progressive principles and generally criticizes traditional institutions.
- Center-left: The text has a slight progressive inclination while maintaining some balance.
- Centrist: The text tries to be impartial without favoring political sides.
- Center-right: The text shows a slight tendency to defend more conservative values.
- Right: The text strongly advocates traditional and conservative values, often opposing progressivism.

The extension justifies the classification based on specific passages, such as explicit support for politicians, parties, or ideologies that suggest a clear political bias.

4. Vocabulary Neutrality
Here, the tool evaluates whether the vocabulary used in the text is neutral or contains many subjective adjectives. The use of loaded language can indicate bias, making this analysis essential for understanding the objectivity of the news. The possible classifications are:

- Highly subjective: The text is filled with adjectives and emotionally charged terms.
- Moderately subjective: The text contains some subjective adjectives but maintains a reasonable level of neutrality.
- Slightly subjective: The text uses few emotional adjectives and is almost neutral.
- Neutral: The vocabulary is completely objective, with no emotionally charged adjectives.

The algorithm highlights words and phrases like \"corrupt,\" \"brilliant,\" or \"betrayal\" to justify whether the vocabulary tends to exaggerate the characteristics or actions of people and events.

5. Conspiracy Narrative Detection
A key function of the extension is detecting conspiracy theories. This module examines whether the text promotes ideas suggesting secret manipulation or orchestrated plans by elite groups to influence major events. The classification categories include:

- High presence: The text is heavily based on conspiracy theories.
- Moderate presence: The text contains conspiracy elements but does not make them the central theme.
- Low presence: There are only slight hints of conspiracy theories.
- No presence: The text is entirely free of conspiracy narratives.

The analysis justifies the presence of these narratives by highlighting sections that indicate elite control, global manipulation, or other typical ideas of conspiracy theories, such as the artificial creation of pandemics.

6. Balance Between Opinion and Fact
This analysis checks how well the text balances verifiable facts with the author's personal opinions. The balance between opinion and fact is a crucial metric for determining the credibility of the article. The classifications include:

- Predominantly opinion-based: The text relies heavily on opinions, with few data or facts.
- Slightly opinion-based: The text contains more opinions than facts but still presents some concrete data.
- Balanced: The text maintains a fair balance between opinions and facts.
- Fact-focused: The text is predominantly based on facts, with few expressed opinions.

The extension identifies and justifies the classification by highlighting opinion passages and verifiable data, making it easier for users to understand the proportion between the two.

7. Calls to Action
Finally, the extension checks for the presence of calls to action, where the text encourages readers to take specific action, whether political, social, or economic. The classifications are:

- High presence: The text contains several direct calls to action.
- Moderate presence: There are some calls to action, but they do not dominate the content.
- Low presence: Only one or two discreet calls to action are found.
- No calls to action: The text contains no direct incentives for action.

The extension justifies this analysis by pointing out sections where the author invites the reader to act, such as \"protest against,\" \"support this cause,\" or \"mobilize.\"

Visualization of Results
In addition to textual analysis, the tool stands out for its user-friendly visualization. As shown in the attached image, the results of each metric are displayed in colored bars that range between shades of blue and red, indicating the level of inclination or intensity of each analysis. A black circle accompanies each bar, highlighting the exact point of the classification. This visual presentation makes it easy for users to quickly grasp the results without needing to read lengthy textual explanations, making the tool both functional and aesthetically pleasing.

In summary, the extension is an effective solution for those who want to critically evaluate news content, providing clear insights and justifications for each aspect analyzed.