Track media sentiment with this app
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Sentiment tracking involves the process of computationally identifying and categorizing the opinions expressed in textual content, such as news articles, social media posts, or online reviews. By analyzing language patterns and contextual cues, sentiment analysis algorithms can determine whether the expressed context of a keyword is positive, negative, or neutral.Table of Content
Intro
In today’s interconnected world, where information flows rapidly, quantifying public sentiment has become paramount for businesses, organizations, and also individuals. With the advent of advanced technologies, particularly artificial intelligence (AI) algorithms, analyzing sentiment in mass media publications is not just a possibility, but a powerful tool in getting an objective picture of mainstream mass media (MSM) discourse. In this article, we’ll discuss interactive, online MSM sentiment tracker that you can access for free and without registration at TextVisualization.app
What is Sentiment Tracking?
Sentiment tracking involves the process of computationally identifying and categorizing the opinions expressed in textual content, such as news articles, social media posts, or online reviews. By analyzing language patterns and contextual cues, sentiment analysis algorithms can determine whether the expressed context of a keyword is positive, negative, or neutral.
Sentiment search tab is an access point to a RESTful API that enables users to get historical media sentiment data and keyword centrality scores for any given word or topic of interest. Go on the front page of TextVisualization.app. Enter a word in Sentiment search and explore the word’s sentiment and role in media narratives
We collect data from mass media publications and use AI algorithms to calculate sentiment score averages for all instances of a keyword within a given timeframe. These sentiment scores quantify the overall tone surrounding the keyword, allowing the use to instantly see media coverage shifts.
Understanding Polarity Sentiment
The first measurement that the media sentiment tracker app shows is called ‘polarity sentiment’. It reflects the overall circumstances associated with a particular keyword over time. A higher polarity sentiment score indicates a generally optimistic, favorable, nonviolent, even a happy context, while a negative score suggests a pessimistic, unfavorable, unhappy sentiment. Such contexts are associated with suffering caused by war or crime, media outrage at a particular topic, and so on.
For instance, when tracking mentions of political figures like Trump or Biden in mainstream media (MSM), a higher polarity sentiment score would indicate positive sentiments expressed in the context surrounding these individuals, whereas a negative score would signify a more critical or unfavorable perception.
AI plots these scores over time, so that we can visualize trends and fluctuations in media coverage.
Detecting Sentiment Inconsistency
Another important aspect of sentiment analysis is detecting sentiment inconsistency or heterogeneity. AI calculates this metric so that we measure the level of disagreement among mass media sources when discussing a particular topic. A higher curve on the plot indicates more divergence in opinions, whereas a lower curve suggests a more unified consensus among global media outlets.
This analysis provides a way into viewing the variance of sentiment across different instances of a keyword in analyzed news texts. For example, within the same hour across various media outlets, sentiments towards a specific topic or individual can vary significantly, indicating sentiment inconsistency. In such cases, the inconsistency score is higher and so is the line on the interactive plot.
Measuring Subjectivity Sentiment
The app also measures subjectivity sentiment measures, the degree of personal opinion or bias present in media publications. A higher subjectivity score indicates a more opinionated or biased viewpoint, while a lower score suggests a more objective or factual presentation. AI tracks subjectivity sentiment over time, so that we identify keywords, themes, and topics in media discourse where subjective opinions prevail.
Applications and Impact
The applications of sentiment analysis in mass media are vast and diverse. From understanding consumer preferences and product feedback to viewing public opinion on political candidates or social issues, sentiment analysis provides valuable data for a number of use cases.
Our AI-powered sentiment analysis tools help businesses and organizations make data-driven decisions, monitor brand reputation, and adapt crisis response strategies in real-time. Additionally, sentiment analysis facilitates market research, competitive analysis, and crisis management, enabling stakeholders to stay informed and responsive to evolving sentiments. For individuals, such analysis helps to quickly navigate in the news landscape and instantly see where the MSM public discourse is heading.
BIGCRUT words
Sentiment analysis of mass media publications offers a powerful means of understanding public opinion, tracking trends, and making informed decisions in an increasingly dynamic and interconnected world. We’ve selected keywords: Biden, Israel, Gaza, COVID, Russia, Ukraine, Trump, Putin (we call them BIGCRUT words) to observe how mainstream media (MSM) discusses these topics. We monitor these and many other keywords over time, and apply AI to mine textual data into the media’s handling of significant global issues and political figures. We do it in order to explore future perspectives, narratives, and agendas shaping public discourse. But this is not all. There is more data that you can view and analyze. Sentiment Search functionality allows to search history of media sentiment and keyword centrality scores for any word. The search base is constantly growing and there are more and more keywords being tracked and analyzed for sentiment and usage.
Sentiment search
Sentiment search tab is an access point to a RESTful API that enables users to get historical media sentiment data and keyword centrality scores for any given word or topic of interest.
At the front page of TextVisualization.app enter a word in Sentiment search to view and explore the word’s sentiment and role in media narratives.
With a constantly expanding search base, encompassing a wide scope of mass media keywords, you can freely access a wealth of historical sentiment data and usage trends. This comprehensive and ever expanding database allows for thorough analysis and exploration of sentiment dynamics over time. Try search for countries - and you will get a kind of political geography guidebook of how this or that country is covered in global press. Use the search for keywords like, ‘gold’, ‘bitcoin’, ‘recession’ - you will see a quantified summary of media coverage over time.
Sentiment Search functionality allows us to uncover patterns, correlations, and sentiment trends associated with specific keywords or topics. Whether tracking the sentiment surrounding a particular product launch, monitoring public perceptions of political figures or sentiment shifts in response to current events, this tool mines into the flow of public opinion. Furthermore, the inclusion of keyword centrality scores enhances the depth of analysis by identifying the prominence and relevance your search term within the media landscape.
