Tag: natural language processing
Day 5: Part-of-Speech Tagging and Named Entity Recognition
Welcome back peeps as we have already discussed about the tokenization and stop words in our last article so, in this day 5 of Natural Language Processing (NLP) journey! In this blog we will be exploring two important techniques for analyzing text: Part-of-Speech (POS) tagging Named Entity Recognition (NER) 1 – Part-of-Speech (POS) tagging is…
Read MoreDay 4: Stemming and Lemmatization
Stemming and lemmatization are two popular techniques for text pre-processing in natural language processing (NLP) tasks. In this article, we will discuss what stemming and lemmatization are and provide examples to illustrate their application. Stemming is the process of reducing a word to its root or stem form. For example, the stem of the word…
Read MoreDay 3: Tokenization and stopword removal
Tokenization and stop word removal are two important steps in pre-processing text data for natural language processing (NLP) tasks. These steps help to prepare the text data for further analysis, modelling, and modelling training. Tokenization is the process of breaking down a larger piece of text into smaller units, called tokens, which can then be…
Read MoreDay 2: Pre-processing Text Data: Cleaning and Normalization
Pre-processing is an important step in any Natural Language Processing (NLP) project. It involves cleaning and normalizing the text data so that it can be processed effectively by NLP algorithms and models. The aim of pre-processing is to improve the quality of the data and make it easier for NLP algorithms to process. In this…
Read MoreDay 1: 30 days of Natural Language Processing (NLP)
Natural Language Processing (NLP) is a subfield of Artificial Intelligence (AI) that focuses on the interaction between computers and humans using natural language. It is a rapidly growing field that has revolutionized the way computers process, understand, and generate human language. In this blog, we will be exploring what NLP is, its history, and its…
Read MoreUnleashing Emotions: Vader for Sentiment Analysis
VADER (Valence Aware Dictionary and Sentiment Reasoner) is a lexicon and rule-based sentiment analysis library that is specifically attuned to sentiments expressed in social media. It is used for sentiment analysis tasks, especially in social media and online reviews, where the language used can be informal and often contains slang, emoticons, and sarcasm. It uses…
Read MoreSentiment Analysis using TextBlob
Sentiment analysis or opinion mining can be used to gain insights from large amounts of data. It uses natural language processing, text analysis, and computational linguistics to detect and extract emotional content from text-based sources. It is used to determine the attitudes, opinions, and emotions of a speaker or writer with respect to some topic…
Read MoreStep-by-Step Process of Implementing Stemming and Lemmatization in Python?
Install the Natural Language Toolkit (NLTK) library. This library provides a range of tools for natural language processing, including stemming and lemmatization algorithms. You can install it using pip install nltk. Import the necessary functions from the NLTK library. For example, to use the Porter stemmer, you would use the following import statement: from nltk.stem.porter…
Read MoreWhat is the Normal Distribution?
Probability distribution is the function that shows the probabilities of the outcome of an event or experiment. Consider a feature (i.e., column) in a dataframe. This feature is a variable and its probability distribution function shows the likelihood of the values it can take. Probability distribution function are quite useful in predictive analytics or machine…
Read MoreWhat are decorators in Python?
Decorators are used to add some design pattern to a function without changing its structure. Decorators generally are defined before the function they are enhancing. To apply a decorator, we first define the decorator function it is applied to and simply add the decorator function above the function it has to be applied to. For…
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