#textanalysis

[interpol-nyc-documents-blog]

Obstacle 2 – A look at the formal & phonetic structure of the lyrics Part I

A look at the formal and phonetic structure of Paul Banks' lyrics in Interpol song Obstacle 2 reveals that Banks uses many systematic, although not particularly consistent, ways, which are similar in lyric poetry and rapping, to enhance the memorability of lyrics and to make them more cohesive.

Banks' poetic lyrics are often in the form of confessional, not always conscious, stream of thought, and they express a constant urge towards the Other involved in the personal relationship. He deals with angst, love, sex and fear, even vulnerability, but mostly it is set in this monology of thought, which is revelatory about a relationship between two people. Banks uses everyday contemporary urban American English and is often playful with how he uses the language. I think that the way he uses metre and rhyme schemes do not only make the lyrics memorable and cohesive, but these techniques connect the text to the music, to the melody and rhythm as well as being expressive of the atmosphere of the song and the text itself

  Obstacle 2 is from Interpol's first album called Turn on the Bright Lights released in 2001. The lyrics from the first verse of the song read:

  I'm gonna pull you in close

gonna wrap you up tight

gonna play with the braids

that you came here with tonight

I'm gonna hold your face

and toast the snow that fell

because friends don't waste

wine when there's words to sell

  –       First verse from Obstacle 2, Lyrics by Paul Banks

  Now we can look more closely what are the formal qualities of this text. The lines are cut differently than they are printed in the Japan album insert. I took the liberty because Banks has himself said that he works from the music and then it is only natural to cut the lines according to the vocal melody. This actually reveals much better how the text works formally as well as phonetically. Every other line has at its ending either a consonance/assonance or a strict rhyme. Strict rhyme has the most powerful effect phonetically and here it strongly binds every other line together. We can call these lines with strict rhyme 'A'. Lets look it by deleting every other line which do not have strict rhyme at the end, the underlining marks the strict rhyme:

             gonna wrap you up tight  =phonetic representation [a I t] 

that you came here with tonight  = [a I t] 

and toast the snow that fell  = phonetic representation [e l]

wine when there's words to sell  = [e l]

  The rhyming also puts weight on these specific words. The consonance appears in the first line and then on the third line. Consonance, which can be called 'C' in this case, is a way of sound patterning where the end of the word, called coda, is repeated in the parallel word's coda:

  I'm gonna pull you in close  = phonetic repr. [s], the 'e' being silent

gonna play with the braids  =  [s]

  Lastly we have the case 'B', which is on the last words of the lines five and seven, called assonance, which involves only the middle of the word, called nucleus. Assonance has actually stronger impact than consonance, but weaker than strict rhyme:

  I'm gonna hold your face  = phonetic repr. [e I]

because friends don't waste  = [e I]

  Now we can find the rhyme scheme Banks has used in this verse of the song, employing different types of rhyme. The scheme is 'CACABABA':

  C  I'm gonna pull you in close

A  gonna wrap you up tight

C  gonna play with the braids

A  that you came here with tonight

B  I'm gonna hold your face

A  and toast the snow that fell

B  because friends don't waste

A  wine when there's words to sell

  Although this is not a traditional rhyme scheme it is very architectural in a way, very strict. There is certain commanding pathos to it. It is merciless in its urge which the rhyming expresses. This mercilessness becomes even more apparent when the metre is taken into account. Metre creates the rhythm of the stanza. This systematic rhyme scheme does not certainly mean that Banks used it consciously. People who are gifted with words, especially writers and poets, do not have to work methodically, because intuition and creativeness is enough, and even rhyme schemes and metre can appear in a creatively produced text almost without any conscious effort. This does not mean that it is easy, or that anyone can do it. You have to be completely accustomed with the world of words and language in order to be successful.

  Part two about syllabic structure and vocal delivery in these lyrics to follow shortly.

Analysis by:

Interpol Documents ©️

[archiveroomb]

Exercise 2

Working with Voyant Tools to Create Data Visualizations

For this exercise, I experimented with Voyant Tools and used Dostoevsky's novella Notes from Underground, sourced from Project Gutenberg.

I've actually read this novella in my first year Philosophy class, I read it again from time to time.

I chose this text because Fyodor Dostoevsky's novella is unique case study. The protagonist, the infamous "Underground Man" is extremely self-aware, but trapped and constantly contradicts himself. So, if there is any literary work that could benefit from having the linguistic pattern quantified and visualized, I thought this would be a good one.

With a document that contains a total of 44,481 total words and 7,479 unique word forms, the corpus was substantial enough to see patterns, but still remain meaningful for my first distant reading method.

First Impressions: The Interface

A feature I found to be distinct in Voyant Tools, is its multi-panel dashboard. Each corner of the site displays a different tool I could reorganize and customize. I actually thought it was just a visual quirk. But now I find it as a philosophical approach to text analysis. The multiple perspectives need to be visible simultaneously, which can help with comparative thinking.

Each panel can be swapped and interactive, which allows me to customize my analytical workspace. Akin to a digital historian's workstation.

The design of the interface is a methodological lesson, and my analysis worked best when I could see the different types of evidence side by side.

Source Criticism

Before any analysis could start, I had to clean the data. The Summary Tool displayed an immediate issue with my corpus and that is the word "gutenburg", which appeared 75 times. This was not in Dostoevsky's lexicon, but a metadata embedded by Project Gutenberg.

My distant reading began with criticising my source by filtering out administrative words. The Stopwords Tool is in charge of this, which affects the word cloud and the frequency statistics in real time. This digital cleaning tool is equivalent to removing stamps or other residues from a physical source or document.

All sources, digital or not, often come with baggage.

Cirrus and Document Terms

After cleaning the data, I analyzed Cirrus (word cloud) and the Document Terms tool to identify which words appeared frequently.

"Man" used 107 times.

"know" used 88 times.

"time used 75 times.

"like" used 75 times.

"love" used 67 times.

I think this highlights the narrators preoccupations: his identity, epistemology, temporality and affect.

The world cloud allowed me to take this data and turn it into a visual map. With the narrator using "man" 107 times, it meant more than just a word. This repetition shows me that questions of identity, self, and what it means to be human are central in the novella. I could narrow my focus for close reading, and philosophical or historical questions.

The Context Tool was like a highlighter, finding every single time that the word "man" was used and displaying sentences it appeared in. For instance, "I am a sick man", found in Part I.

This data no longer showed me a number but a pattern where the narrator used it to describe himself negatively. My analysis concluded that the Underground Man's negative labels for himself might reveal notions of masculinity and identity in 19th-century Russia. The Context Tool didn't directly answer this for me but now I have textual evidence. I could focus on interpretation.

Fancy Visual Voyant Tools: Knots and Dreamscape

After trying the basic tools, I jumped into the fancier tools Voyant offered, such as Knots and DreamScape. These were the most appealing to me.

Knot is a tool that turns the words into spiral or twisty lines. The frequency of the word's usage, the more twists and turns the line will display. The word "know" used 88 times, had more tangles, compared to "man", used 107 times. It's a weird but also cool way of seeing the repetition without the use of numbers.

Discovering the DreamScape Tool was the most exciting for me. It's aim is to find a location's name within the corpus text and dot it in a map. I originally thought it would show me Russia, since the context of the novella takes place there. Instead, it showed multiple locations in North America, places that were not in the book at all.

Before I experimented with the tool it did give me a warning prompt about the tool undergoing experimentation and "do not trust this data" on the Voyant Tools Help page. This taught me to always double-check what the digital tools show me.

Just because a digital tool looks cool, it doesn't speak for its reliability. If Voyant did not show a warning, I would have plainly believed the tool's capabilities of factual data.

For a historian, one shouldn't plainly believe any information at face value. We are surrounded with forged or fabricated documents. It exists in the digital world too.

Final Thoughts

I think Voyant is a helpful pair of glasses for near-sightedness, but not a magnifying glass. If I take the glasses off, I can read the small prints myself, analyse the tone and feel what the underground man is trying to tell me.

I think Voyant is a tool that can be used at the beginning of any project that could help me decide where my focus should be. But the "thinking" job is for me to do on my own with the data it can provide for me.

The limitation of this digital tool is that the real work of understanding people and meanings still remains in the hands of an old-fashioned approach.

[patriciamorris01]

Empowering businesses with intelligent NLP solutions

With Vee Technologies NLP capabilities, your company can quickly and efficiently scour text or recorded speech without the aid of valuable human resources, saving time and reducing costs. Using token classification, entity and relation extraction, named entity recognition (NER), entity classification, and contextual analysis, their NLP services can accomplish a wide variety of tasks for your organization.

Know More: https://www.veetechnologies.com/services/it-services/artificial-intelligence/natural-language-processing.htm

[sudarshannarwade]

NLP Tools for Text Analysis

NLP tools automate the analysis of text for patterns and sentiments.

They include features for text mining, sentiment analysis, and language interpretation.

Key tools offer scalability, accuracy, and integration with various platforms.

Used widely in marketing, finance, healthcare, and research. read more

[arifmehreli]

[writerdanielle]

Unlock the potential of ethical text analysis with the Doxfore5 Python code. Our step-by-step guide simplifies the process, ensuring a seamless journey to insightful analysis

[techninja]

Unleashing the Potential: Exploring the Natural Language Processing (NLP) Market

Natural Language Processing (NLP) technology is revolutionizing the way we interact with machines and process vast amounts of textual data. From virtual assistants to sentiment analysis tools, NLP is reshaping industries across the globe. The NLP market is experiencing unprecedented growth, fueled by advancements in artificial intelligence and the increasing demand for sophisticated language understanding capabilities.

One of the key drivers of the NLP market is the exponential growth of unstructured textual data. With the proliferation of social media, online reviews, and customer feedback, organizations are inundated with vast amounts of text that hold valuable insights. NLP enables machines to understand and interpret this data, extracting actionable insights and improving decision-making processes.

In the healthcare sector, NLP is revolutionizing patient care and clinical research. By analyzing medical records, NLP algorithms can identify patterns and trends, aiding in diagnosis and treatment planning. Similarly, in the financial industry, NLP is used for sentiment analysis of news articles and social media posts, helping traders make informed investment decisions.

The rise of virtual assistants and chatbots is another significant driver of the NLP market. These AI-powered tools leverage NLP technology to understand user queries and provide relevant responses in natural language. From customer service to personalized recommendations, virtual assistants are becoming ubiquitous across various industries, driving the demand for NLP solutions.

Moreover, the advent of deep learning techniques has significantly enhanced the capabilities of NLP systems. Deep learning algorithms, such as recurrent neural networks (RNNs) and transformers, enable machines to grasp the nuances of human language with unprecedented accuracy. As a result, NLP applications are becoming more sophisticated, capable of understanding context and generating human-like responses.

Looking ahead, the future of the NLP market is promising. As organizations increasingly rely on data-driven insights to gain a competitive edge, the demand for NLP solutions will continue to grow. Key market players are investing in research and development to develop more advanced NLP algorithms and applications, catering to the evolving needs of businesses across industries. In summary, the NLP market is poised for remarkable expansion, driving innovation and transforming the way we interact with technology.

[finance-pro]

What is Chat GPT and how to it works?

OpenAI's ChatGPT is a big language model that produces responses to textual cues in natural language that resemble those of humans. It belongs to the GPT (Generative Pre-trained Transformer) family of models, which are made to comprehend and produce writing that resembles what a human would write by foretelling the next word in a string of text.

Based on its training on a vast corpus of text, ChatGPT processes the input prompt and generates a string of words that are likely to follow the prompt. It makes use of a transformer architecture, which enables it to recognize the connections between words in a phrase and produce logical, contextually relevant responses.

The model has been pre-trained on enormous volumes of text data, including web pages, books, and articles, to learn linguistic patterns and structures. The model gains the ability to forecast the most likely term given the words that have come before it during the training phase. Because of this method, it may respond to various inputs and topics, from simple talks to more involved debates.

ChatGPT generates a response when a user enters a text prompt using the information it has learned. The model produces a list of words, which decoding algorithms translate into natural language responses. These algorithms construct a meaningful phrase that is grammatically sound and appropriately contextualized by choosing the words that are most likely to occur together.

Learn more about Chat GPT here.