# Load necessary libraries

import spacy

from spacytextblob.spacytextblob import SpacyTextBlob

import numpy as np

import pandas as pd

# Load spacy model and add textblob pipeline

$nlp=$ spacy. load $(\text{'}$en$\_$core$\_$web$\_$sm$\text{'})$

nlp$.$add$\_$pipe$(\text{'}$spacytextblob$\text{'})$

# Read the csV file

low$\_$memory$=$False$)$

# Remove missing values and replace NaN with an empty string

clean$\_$data $=$ dataframe.dropna$($subset$=[\text{'}$reviews$.$text'$]).$fillna$(\text{'}\text{'})$

# Function to preprocess text

def preprocess$\_$text$($text$)$:

if isinstance$($text$,$ str$)$:

doc $=nlp($text$)$

tokens $=[$token$.$text. Lower$().$strip$()$ for token in doc if not

token.is$\_$stop$]$

return $\text{'}\text{'}.$join$($tokens$)$

else:

return $\text{'}\text{'}$

# Apply preprocessing to the 'reviews.text' column

clean$\_$data$[\text{'}$reviews$.$text'$]=$ clean$\_$data$[\text{'}$reviews$.$text'$].$apply$($preprocess$\_$text$)$

# Function for sentiment analysis

def analyze$\_$sentiment$($review$)$:

doc review