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In this tutorial, You'll learn how to generate new Hindi Text using iNLTK and Python. NLG (Natural Language Generation) can be done in two ways here:1. Get S...
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Aug 21, 2020 · sent2 = 'मैं ऐसे भोजन की सराहना करता हूं जिसका स्वाद अच्छा हो।' doc1 = nlp_hi(sent1) doc2 = nlp_hi(sent2) # Both the sent1 and sent2 are very similar, so, we expect their similarity score to be high. doc1.similarity(doc2) # prints 0.86. Now, let's use these embeddings to find synonyms of a word.
- Dataset For Hindi Text Analysis
- Remove All Emojis from Hindi Text Analysis Data
- Generating Tokens For Hindi Text Analysis
- Remove Stopwords and Punctuations
- Plotting Distribution of Every Tweet’S Length For Each Label
- Remove Most Frequent Unnecessary Words from Hindi Text Analysis Data
- Remove Least Common Words/Tokens
- Plotting Distribution of tweet-length Per Label
- Word Cloud For Hindi Text Analysis
- Conclusion
In this article, we are going to use a large dataset of Hindi tweets from Kaggle. The dataset has over 16000 tweets (including both sarcastic and non-sarcastic) in Hindi. Please note that we will not classify the tweets as sarcastic or non-sarcastic. We will simply use the tweet text to understand how Hindi text processing is performed. With the he...
1. Removing emojis become very easy by using a regular expression for a range of emojis like the one shown below: 2. Let’s remove those emojis from the text and see the word count: 3. If you run df.tail(10), the output would be like this: 4. And with that, all the emojis have been removed from your dataframe! Good job so far. You have successfully ...
Simply put, a token is a single piece of text and tokens are the building blocks of Natural Language processing. Thankfully we have NLP libraries that can gracefully take care of tokenizing the text within seconds and very little code! 1. For generating tokens, I am using indic nlp libraryand the code looks something like this: 2. Let us take a tex...
Since the text contains majorly Hindi words, removing Hindi stop words is inevitable. But if you see carefully, there are some English words used in the tweets too sometimes. Therefore, to be on the safer side, I am going to remove both Hindi and English stopwords. Also, punctuation marks add no value to text analysis. Hence, we are going to remove...
Let’s make more use of word_count to understand something more. To plot a histogram for both labels use the following code: The output histogram looks like this: See here that the length of non_sarcastic tweets is way more than sarcastic tweets. Our efforts today will probably result in a more equal graph for both labels (hint: we’ll successfully d...
Removing just the stopwords and punctuations is not enough at all! We will now look at the frequency of all the words or tokens and remove the absurd or the most unnecessary words. 1. To generate the occurrence frequency of each unique token we will use the code as shown below: 2. The output will be very large since our dataset has almost 16000 ent...
Now is the time to remove the least common tokens from the text corpus. The least occurring tokens or words also add no value to the overall analysis. For this purpose, we will use this code below: You will see the dataframe like this with reduced word_count:
Now let us plot the tweet lengths for both labels and see if our analysis made any difference. The plot would look like this: Figure 7: Distribution of each tweet-length per Label after cleaning data Voilà! The distribution looks good.
The Word cloud will show you the most frequently occurring words and give you an idea of what people have been talking about. Removal of unnecessary tokens in the previous steps has put important topics in the limelight. But running the above code will generate a word cloud like this: All right, what is this? You must be wondering. Well if you look...
In this article, we have covered the step-by-step process to clean Hindi text data. As you can see the process is different from English text cleaning but nevertheless, having sound knowledge of English text processing will be useful any day. I hope this article was helpful to you. As a next step, you can try to design and build a model that would ...
Feb 17, 2023 · In this post, I'll walk you through the process of building your own GPT-3 prompt generator, and show you how to use the tool to generate responses in a wide variety of styles and tones. Prerequisites: Before we begin, you'll need the following: A Python 3.7 or later installation; An OpenAI API key (you can sign up for one at https://beta ...
Aug 29, 2024 · In this tutorial, you will learn how to perform Python translation of nearly any type of text. I’ll show you how to work with the Google Translate and DeepL engines using Python, how to detect the language of your texts, and how to automate language translation using a dedicated TMS.
Dec 30, 2022 · In these articles, We will write python scripts to translate English word to Hindi word and bind it with the GUI application. We are using the English-to-Hindi module to translate the English word into the Hindi word.
People also ask
How to generate text with Python?
Can Python recognize Hindi words?
What is Tkinter Python?
How can we generate text with Python? This article will address these questions and more. How is text generation possible? If you compare natural languages such as Chinese, French or German, they differ in writing, dialects, semantics, and syntaxes.
