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Table 5 presents the keywords that contribute to establishing specific news values in NYT\u2019s reports on the Covid-19 pandemic in the US and in other countries. It can be seen that Eliteness and Personalization are highlighted more in NYT\u2019s domestic news, and Negativity, Impact, and Proximity are more salient in its international news. Superlativeness and Positivity are constructed with similar (low) frequencies. The construction of these news values through keywords will be discussed in the following. Table 3 presents the keywords that contribute to establishing specific news values in CD\u2019s reports on the Covid-19 pandemic in China and in other countries.<\/p>\n<\/p>\n
For example, the Chinese President Xi Jinping is usually mentioned with positive or neutral descriptions whereas the former American President Trump is often represented in a negative way (Examples 10 and 11). Eliteness is also construed through references to central and local governmental institutions such as \u2018commissions\u2019, \u2018local authorities, \u2018The White House\u2019, \u2018The FDA\u2019 and \u2018CDC\u2019, which often serve as sources of information. Meanwhile, consistent with the representations of previous infectious disease outbreaks such as Ebola, SARS, and AIDS, Western media\u2019s coverage of Covid-19 is characterized by labeling the virus as a foreign disease and threat.<\/p>\n<\/p>\n
It demonstrates how US and Chinese media converge and diverge in covering Covid-19 in their home countries and other countries. However, CD and NYT differ in terms of Positivity and Superlativeness, and CD portrays a more positive Self and a more negative Other than NYT does. Personalization is slightly more emphasized in CD\u2019s domestic reports than in its international reports. The most frequent keywords categorized as pointers to Personalization are general references such as \u2018patients\u2019 and \u2018residents\u2019. The concordance analysis shows that of 60 instances of \u2018patients\u2019, 19 are correlated with positive expressions like \u2018recovered\u2019, \u2018discharged\u2019, \u2018released\u2019, and \u2018cured\u2019, highlighting the patients\u2019 recovery from the disease (Example 12). As mentioned before, the keyword \u2018medical\u2019 can be combined with other words to refer to \u2018medical workers\/team\/staff\u2019, thus constructing the news value of Personalization.<\/p>\n<\/p>\n
A marketer\u2019s guide to natural language processing (NLP).<\/p>\n
Posted: Mon, 11 Sep 2023 07:00:00 GMT [source<\/a>]<\/p>\n<\/div>\n Retently discovered the most relevant topics mentioned by customers, and which ones they valued most. Below, you can see that most of the responses referred to \u201cProduct Features,\u201d followed by \u201cProduct UX\u201d and \u201cCustomer Support\u201d (the last two topics were mentioned mostly by Promoters). Predictive text, autocorrect, and autocomplete have become so accurate in word processing programs, like MS Word and Google Docs, that they can make us feel like we need to go back to grammar school. You often only have to type a few letters of a word, and the texting app will suggest the correct one for you. And the more you text, the more accurate it becomes, often recognizing commonly used words and names faster than you can type them. The use of voice assistants is expected to continue to grow exponentially as they are used to control home security systems, thermostats, lights, and cars \u2013 even let you know what you\u2019re running low on in the refrigerator.<\/p>\n<\/p>\n In NYT\u2019s reports, Proximity is constructed through references to locations that are geographically or culturally close to American readers. It is more foregrounded in NYT\u2019s coverage of the pandemic in other countries than in its domestic news. In NYT\u2019s reports on the pandemic in the US, Proximity is names of domestic states or cities (\u2018New York\u2019, \u2018Texas\u2019, etc.) are mentioned. In NYT\u2019s coverage of the pandemic in other countries, countries that are culturally close to the US are mentioned more frequently than those that are geographically proximate. The former group includes Britain, Europe, Italy, Australia, France, Germany, Spain and New Zealand, among which Britain receives the most frequent coverage.<\/p>\n<\/p>\n It involves filtering out high-frequency words that add little or no semantic value to a sentence, for example, which, to, at, for, is, etc. However, since language is polysemic and ambiguous, semantics is considered one of the most challenging areas in NLP. Other practical uses of NLP include monitoring for malicious digital attacks, such as phishing, or detecting when somebody is lying.<\/p>\n<\/p>\n Intermediate tasks (e.g., part-of-speech tagging and dependency parsing) have not been needed anymore. At the moment NLP is battling to detect nuances in language meaning, whether due to lack of context, spelling errors or dialectal differences. Topic modeling is extremely useful for classifying texts, building recommender systems (e.g. to recommend you books based on your past readings) or even detecting trends in online publications. A couple of years ago Microsoft demonstrated that by analyzing large samples of search engine queries, they could identify internet users who were suffering from pancreatic cancer even before they have received a diagnosis of the disease. (meaning that you can be diagnosed with the disease even though you don\u2019t have it).<\/p>\n<\/p>\n <\/p>\n <\/a><\/p>\n The evolution of NLP toward NLU has a lot of important implications for businesses and consumers alike. Imagine the power of an algorithm that can understand the meaning and nuance of human language in many contexts, from medicine to law to the classroom. As the volumes of unstructured information continue to grow exponentially, we will benefit from computers\u2019 tireless ability to help us make sense of it all. While natural language processing isn\u2019t a new science, the technology is rapidly advancing thanks to an increased interest in human-to-machine communications, plus an availability of big data, powerful computing and enhanced algorithms.<\/p>\n<\/p>\n It helps developers to organize knowledge for performing tasks such as translation, automatic summarization, Named Entity Recognition (NER), speech recognition, relationship extraction, and topic segmentation. Government agencies are bombarded with text-based data, including digital and paper documents. Today\u2019s machines can analyze more language-based data than humans, without fatigue and in a consistent, unbiased way. Considering the staggering amount of unstructured data that\u2019s generated every day, from medical records to social media, automation will be critical to fully analyze text and speech data efficiently. Many natural language processing tasks involve syntactic and semantic analysis, used to break down human language into machine-readable chunks.<\/p>\n<\/p>\n Different from print newspapers, online news portals feature an entering page where only headlines and leads hyperlinked to full news reports are presented. In order to attract audiences to read the whole story, journalists may present the most \u2018newsworthy\u2019 information in the headlines and leads. Second, English-language hard news reporting features the \u201cinverted pyramid\u201d structure, an arrangement by which \u201cwhat is \u2018most important information\u2019 comes first and what is less important comes after\u201d (Thomson et al., 2008, p.8). The headline\/lead nucleus therefore constitutes the \u201cmost important news element of the story\u201d (Cotter, 2010, p. 162) and sets up the newsworthy focus or \u2018angle\u2019 of a reported event (White, 1997). The headline (including the sub-heading if there is any) of a news report can be identified without any difficulty, and the lead is usually located immediately below the headline and before the main text.<\/p>\n<\/p>\n It has been around for some time and is very easy and convenient to use. The size and color of each word that appears in the wordcloud indicate it\u2019s frequency or importance. We can observe that the bigrams such as \u2018anti-war\u2019, \u2019killed in\u2019 that are related to war dominate the news headlines. Analyzing the amount and the types of stopwords can give us some good insights into the data. Stopwords are the words that are most commonly used in any language such as \u201cthe\u201d,\u201d a\u201d,\u201d an\u201d etc.<\/p>\n<\/p>\n Topic modeling, sentiment analysis, and keyword extraction (which we\u2019ll go through next) are subsets of text classification. Deep-learning models take as input a word embedding and, at each time state, return the probability distribution of the next word as the probability for every word in the dictionary. Pre-trained language models learn the structure of a particular language by processing a large corpus, such as Wikipedia. For instance, BERT has been fine-tuned for tasks ranging from fact-checking to writing headlines. To conclude, this study contributes new insights into mainstream media story-telling of Covid-19 in different countries by expanding beyond existing work to look at the Self- versus Other- representation.<\/p>\n<\/p>\n In-Text Classification, our aim is to label the text according to the insights we intend to gain from the textual data.<\/p>\n<\/p>\n Semantic Analysis helps machines interpret the meaning of texts and extract useful information, thus providing invaluable data while reducing manual efforts. Collocated with numerals, the keywords \u2018surpass\u2019, \u2018surpassed\u2019, \u2018topped\u2019, and \u2018reached\u2019 are also used to construct Superlativeness. 2 and 3 show that of the twenty-two occurrences, sixteen are followed by a large number. This functions to represent the Covid-19 pandemic in other countries as affecting a large number of people. Besides, many occurrences are used to describe the serious status quo of the pandemic in the US.<\/p>\n<\/p>\n You use a dispersion plot when you want to see where words show up in a text or corpus. If you\u2019re analyzing a single text, this can help you see which words show up near each other. If you\u2019re analyzing a corpus of texts that is organized chronologically, it can help<\/a> you see which words were being used more or less over a period of time. You can learn more about noun phrase chunking in Chapter 7 of Natural Language Processing with Python\u2014Analyzing Text with the Natural Language Toolkit. You\u2019ve got a list of tuples of all the words in the quote, along with their POS tag.<\/p>\n<\/p>\n The Future of AI Education: Great Learning’s Cutting-Edge AI Curriculum.<\/p>\n Posted: Tue, 31 Oct 2023 11:12:49 GMT [source<\/a>]<\/p>\n<\/div>\n To fill the gap, this study will explore how China Daily (CD) and The New York Times (NYT), two mainstream newspapers from China and the US, represent the Covid-19 pandemic in their home countries and in other countries from the perspective of new values. This is why we need a process that makes the computers understand the Natural Language as we humans do, and this is what we call Natural Language Processing(NLP). And, as we know Sentiment Analysis is a sub-field of NLP and with the help of machine learning techniques, it tries to identify and extract the insights. Business intelligence tools use natural language processing to show you who\u2019s talking, what they\u2019re talking about, and how they feel. But without understanding why people feel the way they do, it\u2019s hard to know what actions you should take. Natural Language Understanding (NLU) helps the machine to understand and analyse human language by extracting the metadata from content such as concepts, entities, keywords, emotion, relations, and semantic roles.<\/p>\n<\/p>\n Read more about https:\/\/www.metadialog.com\/<\/a> here.<\/p>\n<\/p>\nWhy Does Natural Language Processing (NLP) Matter?<\/h2>\n<\/p>\n
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Natural Language Processing (NLP) Trends in 2022<\/h2>\n<\/p>\n
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Representation of Covid-19 by Chinese and Western Media<\/h2>\n<\/p>\n
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\nThe Future of AI Education: Great Learning’s Cutting-Edge AI Curriculum – DNA India<\/h3>\n
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