Get aspects from a text

get_aspects.py

import spacy
import pandas as pd
import os
DATA_DIR="path to data"

"""Create a list of common words to remove"""
stop_words=["i", "me", "my", "myself", "we", "our", "ours", "ourselves", "you", "your", "yours", "yourself", 
            "yourselves", "he", "him", "his", "himself", "she", "her", "hers", "herself", "it", "its", "itself", 
            "they", "them", "their", "theirs", "themselves", "what", "which", "who", "whom", "this", "that", "these", 
            "those", "am", "is", "are", "was", "were", "be", "been", "being", "have", "has", "had", "having", "do", 
            "does", "did", "doing", "a", "an", "the", "and", "but", "if", "or", "because", "as", "until", "while", 
            "of", "at", "by", "for", "with", "about", "against", "between", "into", "through", "during", "before", 
            "after", "above", "below", "to", "from", "up", "down", "in", "out", "on", "off", "over", "under", "again", 
            "further", "then", "once", "here", "there", "when", "where", "why", "how", "all", "any", "both", "each", 
            "few", "more", "most", "other", "some", "such", "no", "nor", "not", "only", "own", "same", "so", "than",
            "too", "very", "s", "t", "can", "will", "just", "don", "should", "now"]


"""Load the pre-trained NLP model in spacy"""
nlp=spacy.load("en_core_web_lg") 


"""Define a function to extract keywords"""
def get_aspects(x):
    doc=nlp(x) ## Tokenize and extract grammatical components
    doc=[i.text for i in doc if i.text not in stop_words and i.pos_=="NOUN"] ## Remove common words and retain only nouns
    doc=list(map(lambda i: i.lower(),doc)) ## Normalize text to lower case
    doc=pd.Series(doc)
    doc=doc.value_counts().head().index.tolist() ## Get 5 most frequent nouns
    return doc


"""Read the data"""
con=open(os.path.join(DATA_DIR,'reviews.txt'))
rev=con.read()
con.close()


"""Apply the function to get aspects from reviews"""
print(get_aspects(rev))

# running this will yield
['phone', 'battery', 'camera', 'price', 'life']