What is newsworthy about Covid-19? A corpus linguistic analysis of news values in reports by China Daily and The New York Times Humanities and Social Sciences Communications

Superlativeness is constructed, but not highlighted in NYT’s domestic and international news. 5, seven out of nine instances of the keyword ‘continued’ are used to describe a negative action or situation such as ‘The outbreak’s toll continued to rise’ and ‘the continued spread of the virus’. In this way, the news values of Superlativeness and Negativity are constructed simultaneously. Concordance lines containing ‘far’ show that it can be used with comparative forms to intensify the severity of the reported news event (Examples 18 and 19). Negativity is co-construed here in that negative situations are highlighted.

When a human uses a string of commands to search on a smart speaker, for the AI running the smart speaker, it is not sufficient to “understand” the words. But with the advent of new tech, there are analytics vendors who now offer NLP as part of their business intelligence (BI) tools. We will evaluate our model using various metrics such as Accuracy Score, Precision Score, Recall Score, Confusion Matrix and create a roc curve to visualize how our model performed. Now, we will choose the best parameters obtained from GridSearchCV and create a final random forest classifier model and then train our new model. Now, we will convert the text data into vectors, by fitting and transforming the corpus that we have created. It is a data visualization technique used to depict text in such a way that, the more frequent words appear enlarged as compared to less frequent words.

Natural language generation

Automatic keyword analysis provides the researcher with a list of keywords or clusters that are frequent (or infrequent) in a target corpus as compared to a reference corpus (Scott and Tribble, 2006). While most studies choose a very general corpus as a reference, such as the British National Corpus (BNC), it is more desirable for scholars to design a particular reference corpus in order to answer specific research questions. Since this study focuses on how news media cover Covid-19 in their home countries and in other countries, we reciprocally compared the two sub-corpora of CD as well as the two sub-corpora of NYT. The keywords were then ranked according to their keyness values, which were calculated by using the log-likelihood (LL) statistical test. The higher the keyness value, the more frequently a keyword appears in the target corpus than in the reference corpus, and the more statistically significant it is (Partington, 2010).

If asynchronous updates are not your thing, Yahoo has also tuned its integrated IM service to include some desktop software-like features, including window docking and tabbed conversations. This lets you keep a chat with several people running in one window while you go about with other e-mail tasks. Other part-of-speech patterns include verb phrases (“Run down to the store for some milk”) and adjective phrases (“brilliant emerald”). If you stop “cold”AND “stone” AND “creamery”, the phrase “cold as a fish” will be chopped down to just “fish” (as most stop lists will include the words “as” and “a” in them). Left alone, an n-gram extraction algorithm will grab any and every n-gram it finds.

Translation

Whenever you do a simple Google search, you’re using NLP machine learning. They use highly trained algorithms that, not only search for related words, but for the intent of the searcher. Results often change on a daily basis, following trending queries and morphing right along with human language. They even learn to suggest topics and subjects related to your query that you may not have even realized you were interested in.

Extract tokens and sentences, identify parts of speech, and create dependency parse trees for each sentence. Use the structure and layout information in PDFs to improve custom entity extraction performance. Use Google’s state-of-the-art language technology to classify content across media for better content recommendations and ad targeting. This phase scans the source code as a stream of characters and converts it into meaningful lexemes. Named Entity Recognition (NER) is the process of detecting the named entity such as person name, movie name, organization name, or location. For Example, intelligence, intelligent, and intelligently, all these words are originated with a single root word “intelligen.” In English, the word “intelligen” do not have any meaning.

Virtual assistants, voice assistants, or smart speakers

From time to time, we would like to contact you about our products and services, as well as other content that may be of interest to you. By ticking on the box, you have deemed to have given your consent to us contacting you either by electronic mail or otherwise, for this purpose. It also needs to bring context to the spoken words used, and try and understand the “searcher’s”, eventual aim behind the search. This post’s focus is NLP and its increasing use in what’s come to be known as NLP sentiment analytics.

We will use the dataset which is available on Kaggle for sentiment analysis, which consists of a sentence and its respective sentiment as a target variable. This dataset contains 3 separate files named train.txt, test.txt and val.txt. By tokenizing, you can conveniently split up text by word or by sentence. This will allow you to work with smaller pieces of text that are still relatively coherent and meaningful even outside of the context of the rest of the text. It’s your first step in turning unstructured data into structured data, which is easier to analyze.

Advantages of NLP

Not only are there hundreds of languages and dialects, but within each language is a unique set of grammar and syntax rules, terms and slang. When we write, we often misspell or abbreviate words, or omit punctuation. When we speak, we have regional accents, and we mumble, stutter and borrow terms from other languages.

• 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 – even let you know what you’re running low on in the refrigerator.
• Extract tokens and sentences, identify parts of speech, and create dependency parse trees for each sentence.
• Influenced by culture, ideology, political positions and media systems, news outlets in different countries may choose distinctive frames to represent similar or identical issues (Guo et al., 2012).
• Natural language processing ensures that AI can understand the natural human languages we speak everyday.

This means that an average 11-year-old student can read and understand the news headlines. You can visualize and examine other parts of speech using the above function. Even more headlines are classified as neutral 85 % and the number of negative news headlines has increased (to 13 %). VADER or Valence Aware Dictionary and Sentiment Reasoner is a rule/lexicon-based, open-source sentiment analyzer pre-built library, protected under the MIT license. Let’s dig a bit deeper by classifying the news as negative, positive and neutral based on the scores.

Stemming “trims” words, so word stems may not always be semantically correct. When we speak or write, we tend to use inflected forms of a word (words in their different grammatical forms). To make these words easier for computers to understand, NLP uses lemmatization and stemming to transform them back to their root form. Sentence tokenization splits sentences within a text, and word tokenization splits words within a sentence. Generally, word tokens are separated by blank spaces, and sentence tokens by stops. However, you can perform high-level tokenization for more complex structures, like words that often go together, otherwise known as collocations (e.g., New York).

Connecting SaaS tools to your favorite apps through their APIs is easy and only requires a few lines of code. It’s an excellent alternative if you don’t want to invest time and resources learning about machine learning or NLP. Natural Language Generation (NLG) is a subfield of NLP designed to build computer systems or applications that can automatically produce all kinds of texts in natural language by using a semantic representation as input. Some of the applications of NLG are question answering and text summarization. Google Translate, Microsoft Translator, and Facebook Translation App are a few of the leading platforms for generic machine translation. In August 2019, Facebook AI English-to-German machine translation model received first place in the contest held by the Conference of Machine Learning (WMT).

It involves using machine learning algorithms and linguistic techniques to analyze and classify subjective information. Sentiment analysis finds applications in social media monitoring, customer feedback analysis, market research, and other areas where understanding sentiment is crucial. Data generated from conversations, declarations or even tweets are examples of unstructured data. Unstructured data doesn’t fit neatly into the traditional row and column structure of relational databases, and represent the vast majority of data available in the actual world.

Analysis results show that when presenting the pandemic in domestic news, CD tends to highlight Proximity, Positivity, and Personalization, whereas NYT gives more prominence to Eliteness and Personalization. When the pandemic in other countries is presented, CD foregrounds Negativity, Impact, Superlativeness, and Eliteness, whereas NYT focuses on Negativity, Impact, and Proximity. Compared with NYT, CD shows a stronger tendency to adopt positive self-representation and negative other-representation in its coverage of the Covid-19 pandemic. Apart from the analysis results, the significance of the study also lies in its demonstration of the applicability of a corpus linguistic approach to news values analysis.

Gain real-time analysis of insights stored in unstructured medical text. It mainly focuses on the words, phrases, and sentences. POS stands for parts of speech, which includes Noun, verb, adverb, and Adjective.

This gives us a little insight into, how the data looks after being processed through all the steps until now. Change the different forms of a word into a single item called a lemma. Stopwords are commonly used words in a sentence such as “the”, “an”, “to” etc. which do not add much value. Therefore, this is where the Sentiment Analysis Model comes into play, which takes in a huge corpus of data having user reviews and finds a pattern and comes up with a conclusion based on real evidence rather than assumptions made on a small sample of data. While tokenizing allows you to identify words and sentences, chunking allows you to identify phrases.

It’s also worth noting that the purpose of the Porter stemmer is not to produce complete words but to find variant forms of a word. Lemmatization is converting words into their root word using vocabulary mapping. Lemmatization is done with the help of part of speech and its meaning; hence it doesn’t generate meaningless root words.

• It also includes libraries for implementing capabilities such as semantic reasoning, the ability to reach logical conclusions based on facts extracted from text.
• But in a world that is now witnessing the 4.0 version of the industrial revolution, and with new technologies being born or commercially deployed almost daily, there’s an urgency for man and machine to be on the same page.
• Moreover, as fewer keywords pointing to Negativity and Impact are identified in the domestic news than in the international news, both CD and NYT represent the pandemic in their own countries as less negative and impactful than that in other countries.
• Any single document will contain many SVO sentences, but collections are scanned for facets or attributes that occur at least twice.
• Text classification allows companies to automatically tag incoming customer support tickets according to their topic, language, sentiment, or urgency.

Following this line of research, this study proposes a three-pronged corpus linguistic approach to news values analysis that combines keywords list, collocation, and concordance. The significance of the study resides in its focus on the Self- versus Other-representation in the time of the Covid-19 pandemic and the integration of corpus linguistic analysis with news values. A. Sentiment analysis in NLP (Natural Language Processing) is the process of determining the sentiment or emotion expressed in a piece of text, such as positive, negative, or neutral.

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