Text Preprocessing in NLP with Python codes

This article was published as a part of the Data Science Blogathon Introduction Natural Language Processing (NLP) is a branch of Data Science which deals with Text data. Apart from numerical data, Text data is available to a great extent which is used to analyze and solve business problems. But before using the data for analysis or prediction, processing the data is important. To prepare the text data for the model building we perform text preprocessing. It is the very first […]

Read more

Why and how to use BERT for NLP Text Classification?

This article was published as a part of the Data Science Blogathon Introduction NLP or Natural Language Processing is an exponentially growing field. In the “new normal” imposed by covid19, a significant proportion of educational material, news, discussions happen through digital media platforms. This provides more text data available to work upon! Originally, simple RNNS (Recurrent Neural Networks) were used for training text data. But in recent years there have been many new research publications that provide state-of-the-art results. One of […]

Read more

Part 12: Step by Step Guide to Master NLP – Grammar in NLP

This article was published as a part of the Data Science Blogathon Introduction This article is part of an ongoing blog series on Natural Language Processing (NLP). In the previous article, we discussed some basic concepts related to syntactic analysis. In that article, we covered concepts such as parsing, parse trees, and parsers, etc. But we not discussed the concept of grammar in that article. So, In continuation to that article, we will complete a Syntactic analysis in this article. So, […]

Read more

Natural Language Processing – Sentiment Analysis using LSTM

This article was published as a part of the Data Science Blogathon Introduction: This article aims to explain the concepts of Natural Language Processing and how to build a model using LSTM (Long Short Term Memory), a deep learning algorithm for performing sentiment analysis. Let’s first discuss Natural Language processing! Natural Language Processing: Natural Language Processing (NLP) is a subfield of Artificial Intelligence that deals with understanding and deriving insights from human languages such as text and speech. Some of the […]

Read more

Part 10: Step by Step Guide to Master NLP – Named Entity Recognition

This article was published as a part of the Data Science Blogathon Introduction This article is part of an ongoing blog series on Natural Language Processing (NLP). In the previous article, we discussed semantic analysis, which is a level of NLP tasks. In that article, we discussed the techniques of Semantic analysis in which we discussed one technique named entity extraction, which is very important to understand in NLP. So, In this article, we will deep dive into the entity extraction […]

Read more

Part 1: Step by Step Guide to Master NLP – Introduction

This article was published as a part of the Data Science Blogathon Introduction Computers and Machines are great while working with tabular data or Spreadsheets. However, human beings generally communicate in words and sentences, not in the form of tables or spreadsheets, and most of the information that humans speak or write is present in an unstructured manner. So it is not very understandable for computers to interpret these languages. Therefore, In natural language processing (NLP), our aim is to make […]

Read more

Part 4: Step by Step Guide to Master NLP – Text Cleaning Techniques

This article was published as a part of the Data Science Blogathon Introduction This article is part of an ongoing blog series on Natural Language Processing (NLP). In the previous part of this blog series, we complete the initial steps involved in text cleaning and preprocessing that are related to NLP. Now, in continuation of that part, in this article, we will cover the next techniques involved in the NLP pipeline of Text preprocessing. In this article, we will first discuss […]

Read more

Text detection from images using EasyOCR: Hands-on guide

# Changing the image path IMAGE_PATH = ‘Turkish_text.png’ # Same code here just changing the attribute from [‘en’] to [‘zh’] reader = easyocr.Reader([‘tr’]) result = reader.readtext(IMAGE_PATH,paragraph=”False”) result Output: [[[[89, 7], [717, 7], [717, 108], [89, 108]], ‘Most Common Texting Slang in Turkish’], [[[392, 234], [446, 234], [446, 260], [392, 260]], ‘test’], [[[353, 263], [488, 263], [488, 308], [353, 308]], ‘yazmak’], [[[394, 380], [446, 380], [446, 410], [394, 410]], ‘link’], [[[351, 409], [489, 409], [489, 453], [351, 453]], ‘bağlantı’], [[[373, 525], […]

Read more

All You Need to know about BERT

This article was published as a part of the Data Science Blogathon Introduction Machines understand language through language representations. These language representations are in the form of vectors of real numbers. Proper language representation is necessary for a better understanding of the language by the machine. Language representations are of two types: (i) Context-free language representation such as Glove and Word2vec where embeddings for each token in the vocabulary are constant and it doesn’t depend on the context of the word. […]

Read more

Analyzing customer feedbacks using Aspect Based Sentiment Analysis

This article was published as a part of the Data Science Blogathon Introduction With the advancement in technology, the growth of social media like Facebook, Twitter, Instagram has been a platform for the customers to give feedback to the businesses based on their satisfaction. The reviews posted by customers are the globally trusted source of genuine content for other users. Customer feedback serves as the third-party validation tool to build user trust in the brand. For understanding these customer feedbacks […]

Read more
1 6 7 8 9 10 14