How to Build a Relevant Auto Generating Tags Recommendation Model in Python One of the most important features of any blog or website is its ability to recommend relevant tags to users. This not only helps users find related content easily, but it also improves the overall user experience. In this blog post, we’ll show… Read More How to build relevant auto generating tags recommendation model in python
Suppose I have the following dictionary: data = {‘ACCOUNT_CLOSURE’: [‘account closure’, ‘close account’, ‘close bank’, ‘terminate account’, ‘account deletion’, ‘cancel account’, ‘account cancellation’], ‘ACCOUNT_CHANGE’: [‘change my account’, ‘switch my account’, ‘change from private into savings’, ‘convert into family package’, ‘change title of the account’, ‘make title account to family’, ‘help me access the documentation’]} I… Read More operate over values of a dictionary when values are lists
I have tried this using regex, loops and simple functions as well but cannot figure out anything. here are the few codes I have tried. import re fp = open("WABA-crash.txt") re.findall(r"([^.]*?police[^.]*\.)",fp) For reference, I am placing here a few lines of the WABA-crash.txt ",6810,,,,Crash Description,"Turned right from 38th and Nebraska into a gap in traffic.… Read More How to write all the sentences with the word "police" from a txt file
I am trying to remove stopwords in my data with nltk, but after several attempts I am unable to remove the stopwords. The tokenization part of my code works, but I am unable to understand why stopwords does not work. def pre_process(text): # remove special characters and digits text=re.sub("(\\d|\\W|_)+"," ",text) text=re.split("\W+",text) return text text =… Read More Unable to implement nltk.stopwords
this is a sentiment analysis model made with tf-idf for feature extraction i want to know how can i save this model and reuse it. i tried saving it this way but when i load it , do same pre-processing on the test text and fit_transform on it it gave an error that the model… Read More Keep model made with TFIDF for predicting new content using Scikit for Python
I’m trying to only perform Lemmatization on words in a string that have more than 4 letters. The desired output from the following code should be ‘us american’, but I received an invalid syntax error. import nltk from nltk.tokenize import TweetTokenizer lemmatizer = nltk.stem.WordNetLemmatizer() w_tokenizer = TweetTokenizer() wd = w_tokenizer.tokenize((‘us americans’)) [lemmatizer.lemmatize(w) for w in… Read More Python syntax error in list comprehension on string for Lemmatization
Apple Park is one of the most expensive and impressive buildings in the world. -> ["expensive","impressive"] Thank you for your help! >Solution : import nltk nltk.download(‘averaged_perceptron_tagger’) sentence = "Apple Park is one of the most expensive and impressive buildings in the world." tags = nltk.pos_tag(sentence.split(‘ ‘)) adjectives = [w for w, t in tags if… Read More Is there a way to filter out all adjectives from a string and store them in an array?
Using the existing column name, add a new column first_name to df such that the new column splits the name into multiple words and takes the first word as its first name. For example, if the name is Elon Musk, it is split into two words in the list [‘Elon’, ‘Musk’] and the first word… Read More Get first element of tokenized words in a row
I’m trying to run a for loop or .apply using lambdas for my pandas series. Here’s the code: df[‘sentiment_score’] = df.apply(lambda x: analyzer.polarity_scores(x[‘Filtered_text’]), axis=1) What I’m trying to achieve is for each word in df[‘Filtered_text’], apply the analyzer.polartiy_scores(x[‘Filtered_text’]) through the column. An example of what is stored in df[‘Filtered_text’]: [website, needs, convinient, terrible] [filters, mobile,… Read More Running a for loop or .apply with a pandas series