Communication relies on text. Everywhere. TEXT has become a part of daily life with the rise of the internet, and text analysis therefore is essential to its improvement. The data accessible for acquiring insights has risen over the years, from social media chats to online reviews and complaints.
What’s text analysis?
Traditional data analysis uses relational models with preset data properties (so-called structured data). Only 20% of enterprise data is structured, the rest is unstructured free text. Most of the data we encounter online today is unstructured and free text, including 40 million Wikipedia articles (5 million+ in English), 4.5 billion Web pages, 500 million tweets a day, and 1.5 trillion Google queries a year. Humans can’t process so much info alone. Businesses will gain greatly if machines sort data using text analysis models. Examples:
- Regulatory compliance work involves complicated text papers in healthcare, pharma, and finance. They devote 10-15% of their labor to such tasks and waste money.
- Text analytics can improve employee engagement and productivity as part of HR Analytics.
- Spelling, keyword search, synonyms.
- Social media monitoring helps companies understand customers’ needs, wants, and pain areas.
- Improve customer experience using Chatbots and voice recognition.
The next sections explain step-by-step how an NLP engine analyzes data sources.
STEP ONE: NORMALIZATION
Text fragmentation for effective information extraction. This entails identifying sentence, paragraph, and document ends and splitting text into meaningful components (words, phrases,and symbols).
Unstructured data have no pre-defined data type, therefore they can be string, integers, or special characters.
Next, special characters (such contractions and abbreviations) are deleted and replaced with words. “Doesn’t” equals “does not” according to the engine. The output becomes:
STEP TWO: POS TAG
Every word’s morphology is identified. This determines if a token is a noun, verb, or adjective.
3: ENTITY RELATIONSHIP
Gazetteer module normalizes next. It uses standard lexicons, custom lexicons, and custom rules to add to the morphology module’s fundamental properties. “Smart” and “phone” become “smartphone” at this point.
The unknown words module identifies unknown words using suffix patterns and common misspellings. “Charge” replaces “chagre” In English, it contains opening phrases, complex prepositions, and complex adverbs.
STEP FOUR: SYNTAX PARSING
Parsing syntax determines sentence structure.
Understanding word relationships is key to deciphering text. Dates, currencies, numbers, etc. are recognized.
This is illustrated below.
My phone can’t video chat until I update Android…
Android’s video conferencing on my phone…
Android phones couldn’t do video conferencing.
Android is positive whereas My Smartphone is negative. Smartphone is negative, while Android is neutral. Smartphone and Android both have negative connotations.
STEP FIVE: CHAINING
“Sentence chaining” prepares unstructured text for comprehensive analysis. Each sentence’s association to the overarching issue is used to link them.
E.g “Smartphone” and “charging” are unrelated in “Smartphone is charging.” “Smartphone” and “charging” both relate to “is.”
Text Analytics output can be used to solve NLP cases using multiple models. (Not an exhaustive list):
LSTM is a type of Recurrent Neural Net (RNN) neural that can learn long-term dependencies by memorizing input over long/dynamic periods of time. Each neuron (node) has a memory cell with INPUT, OUTPUT, and FORGET gates. Each gate protects information by preventing or allowing its flow.
Input gate: Determines how much previous-layer data is saved in the cell.
Determines how much the next layer knows about this cell’s status.
Forget Gate: Seems odd, but sometimes you should forget. If it’s learning a book, it may need to forget some characters from the previous chapter.
LSTMs can learn complex sequences like Leonardo-Da-works Vinci’s or old music. Most sequence labeling tasks use LSTM.
Transformer is the core of most current NLP advances. 2017 Google introduced it.
“Transformer neural networks use a self-attention technique to recognize all word associations, regardless of position.” It uses a fixed-size context, the previous words.
She found shells on a riverbank.
“Bank” refers to the shore, not a financial institution. Transformer grasps this instantly.
BERT is pre-trained on Wikipedia (2,500 million words!) and Book Corpus (800 million words)
BERT considers left and right word contexts. This bi-directionality helps NLP grasp word context.
BERT is the first unsupervised, bi-directional NLP pretraining system. It was trained on plain text.
E1 – Embedding
Intermediate representation (12 Layer)
BERT performs badly relative to humans when phrase completion tasks demand world knowledge (common sense).
XLNet combines permutation modeling and two-stream self-attention architecture. BERT’s auto-regressive formulation is overcome.
Unshaded words are masked from the model.
Other models, such as ULMFiT (Universal Language Model Fine-Tuning), which is used for text classification, and ELMo (Embeddings from Language Models), are also widely utilized.
Natural language is ambiguous, meaning one term can have multiple meanings and one phrase can be interpreted in different ways, resulting in distinct interpretations.
Another issue is semantic analysis during information extraction. Due to this, users only see a portion of the text. Text comprehension is needed now.
Since the 1960s, when management information systems and BI as a software category and field of practice emerged, numerical data contained in relational databases was emphasized. “Unstructured” text was difficult to process then. TEXT ANALYTICS has surmounted this difficulty with technological advances.
Websites used text-based searches to find documents with user-defined terms or phrases. Text analytics can uncover meaning- and context-based material (rather than just by a specific word). Text analytics can construct enormous dossiers about persons and events. Large datasets based on online news and social media can be used for social network research or counterintelligence. Text analytics may work like an intelligence analyst or research librarian, but with a narrower focus. Text analytics helps email spam filters identify communications that are likely to be SPAM.