Cleaning and preparing text data for analysis, including techniques like tokenization, stemming, and stop word removal.
Tokenization: Breaking down text into individual words or phrases.
Stop word removal: Removing commonly used words that do not contribute to the meaning of the text.
Stemming: Reducing words to their root form (e.g. jumping, jumped, jumps -> jump).
Lemmatization: Reducing words to their base or dictionary form (e.g. went -> go).
Part-of-speech tagging: Identifying the part of speech (noun, verb, adjective, etc.) of each word in a text.
Named entity recognition: Identifying and classifying named entities (e.g. people, organizations, locations) in a text.
Frequency analysis: Identifying the most common words or phrases in a text.
Sentiment analysis: Identifying the emotional tone of a text (positive, negative, neutral).
Text classification: Categorizing texts into predefined categories (e.g. spam/not spam, positive/negative sentiment).
Topic modeling: Identifying topics within a collection of texts.
Word embeddings: Representing words as numerical vectors for machine learning applications.
Dependency parsing: Identifying the grammatical relationships between words in a sentence.
Coreference resolution: Identifying when two or more words refer to the same entity in a text.
Spell checking: Identifying and correcting spelling errors in a text.
Language detection: Identifying the language of a text.
Text normalization: Converting texts to a standard form or language (e.g. converting British English to American English).
Text cleansing: Removing unwanted or irrelevant data from text (e.g. HTML tags or URLs).
Text summarization: Generating a brief summary of a longer text.
Entity linking: Linking named entities in text to their corresponding entries in a knowledge base.
Tokenization: The process of splitting text into smaller chunks, typically words, phrases or sentences.
Stemming: A technique used to reduce words to their base form by removing the suffixes, for example, the stem of "running" is "run".
Lemmatization: A more refined technique than stemming, which reduces words to their canonical form or lemma, preserving the morphological meaning of the word.
Stop Word Removal: A technique used to filter out common and irrelevant words that are typically present in a text corpus.
Part-of-Speech Tagging: A process of assigning specific labels to each word in a text based on its syntactical function, for example, a noun, pronoun, verb, adjective, etc.
Named Entity Recognition: A process of identifying and classifying named entities such as people, organizations, and locations in a text corpus.
Parsing: A process of breaking down a sentence into its grammatical components like subject, verb, and object.
Spell Checking: A technique used to identify and correct misspelled words in a text corpus.
Noise Removal: A technique used to remove unwanted characters, such as punctuation marks, whitespaces, and special characters, that do not add any meaning to the text.
Normalization: A process of transforming text into a standard format, such as converting all text to lowercase or removing accents and diacritics.
Text Encoding/Decoding: A process of converting raw text into machine-readable format using encoding methods like UTF-8, ASCII, etc.
Vectorization: A process of transforming text into numerical features with the help of mathematical models, such as Bag of Words (BoW), Term Frequency-Inverse Document Frequency (TF-IDF), etc.
Topic Modelling: A technique used to extract underlying themes or topics from a text corpus using algorithms like Latent Dirichlet Allocation (LDA) or Non-negative Matrix Factorization (NMF).
Sentiment Analysis: A process of identifying and analyzing the emotional tone or sentiment of a piece of text, such as positive, negative, or neutral using machine learning algorithms.
Text Classification: A process of categorizing text into predefined classes or categories, for example, classifying emails as spam or not spam, classifying news articles based on their topics, etc.