PYTHON BASIC SENTIMENT ANALYSIS USING ALEXA DATA

Basic sentiment analysis using python pandas



#python #pandas #dwbiadda #analysis #repeatedwords



import pandas as pd

alexa_data=pd.read_csv('C:\Data_Set\conv_data\Alexa_rating.csv')

alexa_data.head(6)

alexa_data['desc_conver'] = alexa_data.verified_reviews.str.lower().str.strip().str.split()

alexa_data.head()



stop_words = frozenset(['the', 'as', 'is','i','it','to','and','my','this','for','have','a','but','of','on','in','that','was','so','can','you'])

conv_list_reviews = list()

for data in alexa_data[['variation', 'desc_conver']].iterrows() :

    row_data = data[1]

    for final_word in row_data.desc_conver:

        if final_word not in stop_words:

            conv_list_reviews.append((row_data.variation, final_word))





verified_reviews = pd.DataFrame(conv_list_reviews, columns=['produc_type', 'Repeated_word'])

data_set=verified_reviews[verified_reviews.Repeated_word.str.len()>0]



data_set=data_set.groupby('produc_type').Repeated_word.value_counts().to_frame().rename(columns={'Repeated_word':'Cnt_of_word'})

data_set.head()



def display_top_rec(records, index_lvl=0):

    final_result = records.groupby(level=index_lvl).apply(lambda records: records.nlargest(3,keep='first')).reset_index(level=index_lvl, drop=True)

    return final_result.to_frame()





display_top_rec(data_set['Cnt_of_word'])