I am attempting to answer thefollowing:

Utilize your Python environment to derive structure fromunstructured data. You will utilize the data set "AirlineSentiment" from Kaggle located at Kaggles website. From Welkin10,the dataset "Airline Sentiment". /welkin10/airline-sentinment

Using this data set, you will create a text analytics Pythonapplication that extracts themes from each comment using termfrequency?inverse document frequency (TF?IDF) or simple wordcounts. For the deliverable, provide your Python file and a .csvwith your results added as a column to the original data set.

I have the following code:

import pandas as pdimport numpy as np

#Using sklearn library to calculate tfidffrom sklearn.feature_extraction.text import TfidfVectorizer

#Download datasetdemo_document =pd.read_csv("C:/Users/Documents/Tweets.csv") dataset = []for demo in demo_document[:100]: dataset.append(" ".join(demo))

#Print first 15 documents of the datasetprint("DEMO DATASET")for i in range(15): print(i,dataset[i])

#fit_transform will calculate the idf-idf scoresmodel = TfidfVectorizer(use_idf=True)tfIdf = model.fit_transform(dataset)

#Print tf-idf of first 15 documents of the datasetprint("TF-IDF VALUES:")df = pd.DataFrame(tfIdf[:15].todense(),columns=model.get_feature_names())print (df)

#Export dataframe to .csvexport_df = pd.DataFrame(tfIdf.todense(),columns=model.get_feature_names())export_df.to_csv("C:/Users/Documents/Tweets_data.csv")

I am receiving the following error:

ValueError: empty vocabulary; perhaps the documents only containstop words.

How do I go about resolving this error?

Transcribed Image Text:

Hami ND000 HN 1 A 2 3 4 5 6 7 8 9 10 11 12 13 998722ZHONG 20 21 23 import pandas as pd import numpy as np 14 15 16 17 18 #fit_transform will calculate the idf-idf scores 19 model = TfidfVectorizer (use_idf=True) tfIdf = model.fit_transform(dataset) 24 #Using sklearn library to calculate tfidf from sklearn.feature_extraction.text import TfidfVectorizer 26 #Download dataset demo_document = pd.read_csv ("C:/Users dataset= [] for demo in demo_document[:100]: dataset.append(" ".join(demo)) #Print first 15 documents of the dataset print("DEMO DATASET") for i in range(15): print(i, dataset[i]) /Documents/Tweets.csv") df = pd.DataFrame(tfIdf[:15].todense(), columns=model.get_feature_names()) 25 print (df) #Print tf-idf of first 15 documents of the dataset print("\nTF-IDF VALUES: ") #Export dataframe to .cs .csv export_df = pd.DataFrame(tff.todense(), columns=model.get_feature_names ()) export_df.to_csv ("C:/Users /Documents/Tweets_data.csv") Hami ND000 HN 1 A 2 3 4 5 6 7 8 9 10 11 12 13 998722ZHONG 20 21 23 import pandas as pd import numpy as np 14 15 16 17 18 #fit_transform will calculate the idf-idf scores 19 model = TfidfVectorizer (use_idf=True) tfIdf = model.fit_transform(dataset) 24 #Using sklearn library to calculate tfidf from sklearn.feature_extraction.text import TfidfVectorizer 26 #Download dataset demo_document = pd.read_csv ("C:/Users dataset= [] for demo in demo_document[:100]: dataset.append(" ".join(demo)) #Print first 15 documents of the dataset print("DEMO DATASET") for i in range(15): print(i, dataset[i]) /Documents/Tweets.csv") df = pd.DataFrame(tfIdf[:15].todense(), columns=model.get_feature_names()) 25 print (df) #Print tf-idf of first 15 documents of the dataset print("\nTF-IDF VALUES: ") #Export dataframe to .cs .csv export_df = pd.DataFrame(tff.todense(), columns=model.get_feature_names ()) export_df.to_csv ("C:/Users /Documents/Tweets_data.csv")

Answer rating: 100% (QA)

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