Media Analysis
Unleashing Insights: How NLP Techniques Illuminate Textual Data
Here you can observe the aspects of media analysis, we demonstrate our media analysis process and some of our findings. We explore a variety of topics including political parties, social movements, countries, and companies. We talk about the various aspects of natural language processing, data presentations, its applications, and our plans to develop the technology.
Our Findings
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High Probability of media manipulation
The mentioned findings outlined above encompass a selection of intriguing topics that have undergone our analysis. One notable discovery that has raised concerns pertains to the substantial evidence indicating manipulation in news coverage associated with the Republican Party. This manipulation seems to have commenced shortly after the 1980 election, which saw the victory of Ronald Reagan. The unnaturally linear growth pattern observed in the data stands out distinctly when compared to other graphs, further accentuating its irregular nature. All of the data sets that were used in our research have been published under the datasets tab. We are currently investigating this matter trying to identify whether this is a matter of concern. We are still in the process of identifying the cause of this discrepancy.
Sentimental Analysis
Understanding the Graph: A Simple Guide
Our graph paints a picture of how has been portrayed in the news from 1970 to 2023.
What are we looking at? Imagine every news article as giving a score: a positive score for positive news and a negative score for negative news. Over time, these scores add up, and that's what our graph shows.
There is an example of a negative news article titled, "How China's Debt Problem Could Trigger a Financial Crisis". And the score that is given to this title is, -0.8519, analogically a positive news article title would be given a positive number score. There are several different types of ways to come up with sentiment scores and classifications, one using a set of predefined rules and heuristics to determine the sentiment of a piece of text, with the classificans being determined based on the score (positive, negative, neutral).
How to interpret the graph?
Upward Trend: If the line on our graph moves upwards, it means that, over time, there have been more positive stories about Russia than negative ones.
Downward Trend: On the other hand, if the line descends, it means that there were more negative news stories than positive ones during that period.
Flat Line: If the line is flat, it signifies that the positive and negative stories about balanced each other out.
Why is this important? Understanding how a country is portrayed in the news gives us insights into global perceptions, events of significance, and the evolving narrative over the years. By looking at the trend, you can get a sense of whether the news about Russia has been generally positive, negative, or neutral over the years. Analogically identifying the current state of media representation of a topic gives very important insight into how the majority of people perceive a given topic. As the majority of the population gets their information about an event or entity from media sources, and media portrayal determines what the majority of people think.
Use Cases
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Political Campaigns: Political campaigns can use EmoFlow Timeline to monitor the changing sentiments of the public towards different policies, candidates, and issues. This insight helps strategists adjust their messaging and engagement strategies based on the emotional responses of voters.
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Companies/Brands: Businesses can track public sentiment towards their products and services. By analyzing how sentiments shift over time, they can identify trends, customer preferences, and potential problems. This aids in adapting marketing campaigns and improving customer relations.
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Governments: Governments can gauge public sentiment on policies, initiatives, and social issues. This understanding helps in making informed decisions that align with the needs and sentiments of the population, fostering better governance.
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Celebrity: Celebrities and public figures can analyze public sentiment towards their actions, statements, and projects. This enables them to adjust their public image and engage with fans in ways that resonate positively.
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Research and Academia: Researchers can utilize emotional trend visualizations to study cultural shifts, societal attitudes, and historical changes in sentiment. This aids in fields such as sociology, psychology, and media studies.
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Understanding Current News: News outlets can incorporate emotional trend analysis to provide deeper context to news stories. By showing how public sentiment has evolved in response to events, they can help readers understand the emotional impact of news.
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Content Creation: Authors, screenwriters, and content creators can gain insights into how emotional arcs develop over time. This knowledge enriches storytelling by aligning narratives with emotional expectations.
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Healthcare and Well-being: Emotional trend analysis can be applied in mental health research to understand societal well-being and the impact of interventions on emotional states.
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Crisis Management: During crises or emergencies, organizations can monitor public sentiment to assess the effectiveness of response efforts and identify areas of concern or misinformation.
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Historical Analysis: Historians can use emotional trend visualization to explore how sentiments influenced historical events and shaped the collective mindset of different eras.
Adding Large Language Modeles
We are currently working to implement Large Language Models (LLMs) into the analysis. Although, harnessing the power of sentimental analysis, topic modeling, and entity recognition, we gain valuable insights into the ever-evolving social landscape. However, when we combine these analytical techniques with a robust large language model, a whole new realm of understanding emerges. It is through this synergistic approach that we unlock the ability to identify crucial points and conduct profound analyses. We firmly believe that integrating the LLMs into our methodology represents the next evolutionary leap in comprehending vast volumes of data, enabling us to extract meaningful insights and make impactful observations. LLMs are able to interpret the meaning and a more nuanced approach to pieces of text.
We believe this is a stepping stone in connecting artificial intelligence to the current flow of events. The amount of data being generated about events is far too great for any one person to even fathom, and the artificial gene has the ability to help us make decisions by helping us understand what is happening holistically. And even getting suggestions based on a holistic understanding of current events.
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Development Plans
We are planning on making an open-source project to publish social analysis reports on requested topics, as well as topics current important topics that will hopefully be able to give people an understanding of the current and historical state of media to understand and potentially understand the manipulations of media. In addition, we are currently developing a commercial platform for private use with more extended features, that would be unsustainable to support free publishing.
Datasets
If you are interested in the datasets that we used in this demonstration, they are free to download and use in your own research. If you have any additional inquiries, please feel free to contact me at nkudrin@omni-group.dev.
