Introduction

Nowadays, most organizations and businesses develop online services, which add value to their business and even increase their customer base. Surveys and reviews have changed the dynamics of digital marketing. The feedback platform gave the power to customers to post, share, and review content. Customers can directly interact with other customers and companies.There are many companies that use public opinion to be able to achieve the goals of the company. 20-30 companies in the United States of America offer sentiment analysis as one of the tools to help corporate decision-making. Therefore, we use dynamic sentiment to discover additional information. This research uses dynamic sentiment because it can gather more precise and detailed result.

Along with the increasing number of internet, social media users and mobile devices will certainly impact on the increasing amount of data or user-generated content. The simple forms to collect opinions are application review and rating people give for their experience. With a massive information flows from social media, a highly effective approach is needed to summarize and retrieve information in a real-time situation. Several classification methods are suitable to analyze the data, such as Support Vector Machine (SVM), Naïve Bayes (NB), Nearest Neighbors (NN), Logistic Regression and Decision Tree. The model used to summarize is Naïve Bayes classification method.

We use topic modeling to determine topics that contained in the data. Numerous way of topic distribution is applicable such as Clustering, Feature Generation, and Dimensionality Reduction. Dimensionality reduction has an advantage compared to the other because each document’s distribution over topics gives a summary of the document. Compare them in this reduce feature space can be more meaningful than comparing in the original feature space.

Uber is one of the largest and greatest innovation in transport with its fast development, the interaction among user in the platform is high.In this research, we aim to dig further information from the dataset. We see the topics from the public opinion perspective in application reviews and ratings, especially their opinion regarding Uber, the sentiment is surely changing every day then which topic has positive or negative sentiment. This is useful for data processing to be more effective and fast.

What are research questions?

In this research, we will analyze huge user-generated content which can be used by organizations for their customer engagement strategies.The purpose of this study is to to map the public opinion towards certain topic by analyzing the sentiment of the text and create a topic model. The reviews feedback provide a lot of information about the product experience, any technical or operational gaps, and even their general sentiment towards the product company. The analysis will help in identifying the gaps in the priorities of the stakeholders. With the right customer engagement strategies, companies can make benefit.

We pick Uber as the case study, viewed as one of the most favored transportation methods in most part of the world to do the following search question.

1. Analyse the user’s sentiments with Uber cabs.
2. Problems faced by customers.

How are the data collected?

Collected the data from play store reviews. I scrapped the latest 50,000 reviews from there irrespective of the rating. https://play.google.com/store/apps/details?id=com.ubercab

#installing the required packages
!pip install bertopic
!pip install google-play-scraper 
!pip install keybert

Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
Collecting bertopic
  Downloading bertopic-0.12.0-py2.py3-none-any.whl (90 kB)
     |████████████████████████████████| 90 kB 2.0 MB/s 
Collecting hdbscan>=0.8.28
  Downloading hdbscan-0.8.29.tar.gz (5.2 MB)
     |████████████████████████████████| 5.2 MB 16.7 MB/s 
  Installing build dependencies ... done
  Getting requirements to build wheel ... done
    Preparing wheel metadata ... done
Requirement already satisfied: pandas>=1.1.5 in /usr/local/lib/python3.7/dist-packages (from bertopic) (1.3.5)
Requirement already satisfied: tqdm>=4.41.1 in /usr/local/lib/python3.7/dist-packages (from bertopic) (4.64.1)
Collecting pyyaml<6.0
  Downloading PyYAML-5.4.1-cp37-cp37m-manylinux1_x86_64.whl (636 kB)
     |████████████████████████████████| 636 kB 61.3 MB/s 
Collecting umap-learn>=0.5.0
  Downloading umap-learn-0.5.3.tar.gz (88 kB)
     |████████████████████████████████| 88 kB 7.6 MB/s 
Requirement already satisfied: scikit-learn>=0.22.2.post1 in /usr/local/lib/python3.7/dist-packages (from bertopic) (1.0.2)
Requirement already satisfied: numpy>=1.20.0 in /usr/local/lib/python3.7/dist-packages (from bertopic) (1.21.6)
Collecting sentence-transformers>=0.4.1
  Downloading sentence-transformers-2.2.2.tar.gz (85 kB)
     |████████████████████████████████| 85 kB 4.4 MB/s 
Requirement already satisfied: plotly>=4.7.0 in /usr/local/lib/python3.7/dist-packages (from bertopic) (5.5.0)
Requirement already satisfied: joblib>=1.0 in /usr/local/lib/python3.7/dist-packages (from hdbscan>=0.8.28->bertopic) (1.2.0)
Requirement already satisfied: cython>=0.27 in /usr/local/lib/python3.7/dist-packages (from hdbscan>=0.8.28->bertopic) (0.29.32)
Requirement already satisfied: scipy>=1.0 in /usr/local/lib/python3.7/dist-packages (from hdbscan>=0.8.28->bertopic) (1.7.3)
Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=1.1.5->bertopic) (2022.6)
Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=1.1.5->bertopic) (2.8.2)
Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from plotly>=4.7.0->bertopic) (1.15.0)
Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.7/dist-packages (from plotly>=4.7.0->bertopic) (8.1.0)
Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.22.2.post1->bertopic) (3.1.0)
Collecting transformers<5.0.0,>=4.6.0
  Downloading transformers-4.24.0-py3-none-any.whl (5.5 MB)
     |████████████████████████████████| 5.5 MB 41.6 MB/s 
Requirement already satisfied: torch>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from sentence-transformers>=0.4.1->bertopic) (1.12.1+cu113)
Requirement already satisfied: torchvision in /usr/local/lib/python3.7/dist-packages (from sentence-transformers>=0.4.1->bertopic) (0.13.1+cu113)
Requirement already satisfied: nltk in /usr/local/lib/python3.7/dist-packages (from sentence-transformers>=0.4.1->bertopic) (3.7)
Collecting sentencepiece
  Downloading sentencepiece-0.1.97-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)
     |████████████████████████████████| 1.3 MB 60.8 MB/s 
Collecting huggingface-hub>=0.4.0
  Downloading huggingface_hub-0.11.0-py3-none-any.whl (182 kB)
     |████████████████████████████████| 182 kB 52.5 MB/s 
Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from huggingface-hub>=0.4.0->sentence-transformers>=0.4.1->bertopic) (2.23.0)
Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from huggingface-hub>=0.4.0->sentence-transformers>=0.4.1->bertopic) (3.8.0)
Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub>=0.4.0->sentence-transformers>=0.4.1->bertopic) (21.3)
Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub>=0.4.0->sentence-transformers>=0.4.1->bertopic) (4.1.1)
Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from huggingface-hub>=0.4.0->sentence-transformers>=0.4.1->bertopic) (4.13.0)
Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.9->huggingface-hub>=0.4.0->sentence-transformers>=0.4.1->bertopic) (3.0.9)
Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers>=0.4.1->bertopic) (2022.6.2)
Collecting tokenizers!=0.11.3,<0.14,>=0.11.1
  Downloading tokenizers-0.13.2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.6 MB)
     |████████████████████████████████| 7.6 MB 49.1 MB/s 
Requirement already satisfied: numba>=0.49 in /usr/local/lib/python3.7/dist-packages (from umap-learn>=0.5.0->bertopic) (0.56.4)
Collecting pynndescent>=0.5
  Downloading pynndescent-0.5.8.tar.gz (1.1 MB)
     |████████████████████████████████| 1.1 MB 54.9 MB/s 
Requirement already satisfied: llvmlite<0.40,>=0.39.0dev0 in /usr/local/lib/python3.7/dist-packages (from numba>=0.49->umap-learn>=0.5.0->bertopic) (0.39.1)
Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from numba>=0.49->umap-learn>=0.5.0->bertopic) (57.4.0)
Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->huggingface-hub>=0.4.0->sentence-transformers>=0.4.1->bertopic) (3.10.0)
Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from nltk->sentence-transformers>=0.4.1->bertopic) (7.1.2)
Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->huggingface-hub>=0.4.0->sentence-transformers>=0.4.1->bertopic) (2022.9.24)
Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->huggingface-hub>=0.4.0->sentence-transformers>=0.4.1->bertopic) (2.10)
Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->huggingface-hub>=0.4.0->sentence-transformers>=0.4.1->bertopic) (1.24.3)
Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->huggingface-hub>=0.4.0->sentence-transformers>=0.4.1->bertopic) (3.0.4)
Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.7/dist-packages (from torchvision->sentence-transformers>=0.4.1->bertopic) (7.1.2)
Building wheels for collected packages: hdbscan, sentence-transformers, umap-learn, pynndescent
  Building wheel for hdbscan (PEP 517) ... done
  Created wheel for hdbscan: filename=hdbscan-0.8.29-cp37-cp37m-linux_x86_64.whl size=2340729 sha256=c1f37c5264623fa064b2d8a4a5be7b0fcf730c37cc7856a79e559c00b6defa73
  Stored in directory: /root/.cache/pip/wheels/93/78/2e/03ee191669a772e9653260aa3bd53e0b1a768751a9676e8c82
  Building wheel for sentence-transformers (setup.py) ... done
  Created wheel for sentence-transformers: filename=sentence_transformers-2.2.2-py3-none-any.whl size=125938 sha256=7ed1f9b4e09b0fd2bf5603fb50fa3890eed7b20e0b022f2053e2d25b07cdbc7a
  Stored in directory: /root/.cache/pip/wheels/bf/06/fb/d59c1e5bd1dac7f6cf61ec0036cc3a10ab8fecaa6b2c3d3ee9
  Building wheel for umap-learn (setup.py) ... done
  Created wheel for umap-learn: filename=umap_learn-0.5.3-py3-none-any.whl size=82829 sha256=c1dee8501f8567d7f362c71098e1d68e51f71b85d4acc5462d5ae093f566c4ed
  Stored in directory: /root/.cache/pip/wheels/b3/52/a5/1fd9e3e76a7ab34f134c07469cd6f16e27ef3a37aeff1fe821
  Building wheel for pynndescent (setup.py) ... done
  Created wheel for pynndescent: filename=pynndescent-0.5.8-py3-none-any.whl size=55513 sha256=b9ae24620001c8c5f9f5f0928752546da2b77d47f18f96c61a221952877d842c
  Stored in directory: /root/.cache/pip/wheels/19/bc/eb/974072a56a7082a302f8b4be1ad6d21bf5019235c2eff65928
Successfully built hdbscan sentence-transformers umap-learn pynndescent
Installing collected packages: pyyaml, tokenizers, huggingface-hub, transformers, sentencepiece, pynndescent, umap-learn, sentence-transformers, hdbscan, bertopic
  Attempting uninstall: pyyaml
    Found existing installation: PyYAML 6.0
    Uninstalling PyYAML-6.0:
      Successfully uninstalled PyYAML-6.0
Successfully installed bertopic-0.12.0 hdbscan-0.8.29 huggingface-hub-0.11.0 pynndescent-0.5.8 pyyaml-5.4.1 sentence-transformers-2.2.2 sentencepiece-0.1.97 tokenizers-0.13.2 transformers-4.24.0 umap-learn-0.5.3
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
Collecting google-play-scraper
  Downloading google_play_scraper-1.2.2-py3-none-any.whl (28 kB)
Installing collected packages: google-play-scraper
Successfully installed google-play-scraper-1.2.2
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
Collecting keybert
  Downloading keybert-0.7.0.tar.gz (21 kB)
Requirement already satisfied: sentence-transformers>=0.3.8 in /usr/local/lib/python3.7/dist-packages (from keybert) (2.2.2)
Requirement already satisfied: scikit-learn>=0.22.2 in /usr/local/lib/python3.7/dist-packages (from keybert) (1.0.2)
Requirement already satisfied: numpy>=1.18.5 in /usr/local/lib/python3.7/dist-packages (from keybert) (1.21.6)
Collecting rich>=10.4.0
  Downloading rich-12.6.0-py3-none-any.whl (237 kB)
     |████████████████████████████████| 237 kB 7.0 MB/s 
Requirement already satisfied: typing-extensions<5.0,>=4.0.0 in /usr/local/lib/python3.7/dist-packages (from rich>=10.4.0->keybert) (4.1.1)
Collecting commonmark<0.10.0,>=0.9.0
  Downloading commonmark-0.9.1-py2.py3-none-any.whl (51 kB)
     |████████████████████████████████| 51 kB 6.7 MB/s 
Requirement already satisfied: pygments<3.0.0,>=2.6.0 in /usr/local/lib/python3.7/dist-packages (from rich>=10.4.0->keybert) (2.6.1)
Requirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.22.2->keybert) (1.7.3)
Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.22.2->keybert) (1.2.0)
Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.22.2->keybert) (3.1.0)
Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from sentence-transformers>=0.3.8->keybert) (4.64.1)
Requirement already satisfied: sentencepiece in /usr/local/lib/python3.7/dist-packages (from sentence-transformers>=0.3.8->keybert) (0.1.97)
Requirement already satisfied: huggingface-hub>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from sentence-transformers>=0.3.8->keybert) (0.11.0)
Requirement already satisfied: torchvision in /usr/local/lib/python3.7/dist-packages (from sentence-transformers>=0.3.8->keybert) (0.13.1+cu113)
Requirement already satisfied: torch>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from sentence-transformers>=0.3.8->keybert) (1.12.1+cu113)
Requirement already satisfied: nltk in /usr/local/lib/python3.7/dist-packages (from sentence-transformers>=0.3.8->keybert) (3.7)
Requirement already satisfied: transformers<5.0.0,>=4.6.0 in /usr/local/lib/python3.7/dist-packages (from sentence-transformers>=0.3.8->keybert) (4.24.0)
Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from huggingface-hub>=0.4.0->sentence-transformers>=0.3.8->keybert) (2.23.0)
Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from huggingface-hub>=0.4.0->sentence-transformers>=0.3.8->keybert) (3.8.0)
Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from huggingface-hub>=0.4.0->sentence-transformers>=0.3.8->keybert) (4.13.0)
Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub>=0.4.0->sentence-transformers>=0.3.8->keybert) (5.4.1)
Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub>=0.4.0->sentence-transformers>=0.3.8->keybert) (21.3)
Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.9->huggingface-hub>=0.4.0->sentence-transformers>=0.3.8->keybert) (3.0.9)
Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.7/dist-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers>=0.3.8->keybert) (0.13.2)
Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers>=0.3.8->keybert) (2022.6.2)
Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->huggingface-hub>=0.4.0->sentence-transformers>=0.3.8->keybert) (3.10.0)
Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from nltk->sentence-transformers>=0.3.8->keybert) (7.1.2)
Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->huggingface-hub>=0.4.0->sentence-transformers>=0.3.8->keybert) (2022.9.24)
Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->huggingface-hub>=0.4.0->sentence-transformers>=0.3.8->keybert) (3.0.4)
Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->huggingface-hub>=0.4.0->sentence-transformers>=0.3.8->keybert) (2.10)
Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->huggingface-hub>=0.4.0->sentence-transformers>=0.3.8->keybert) (1.24.3)
Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.7/dist-packages (from torchvision->sentence-transformers>=0.3.8->keybert) (7.1.2)
Building wheels for collected packages: keybert
  Building wheel for keybert (setup.py) ... done
  Created wheel for keybert: filename=keybert-0.7.0-py3-none-any.whl size=23800 sha256=fc580bfbe08afa010890d624b619e7c115a109737bf176d6624274838d927da8
  Stored in directory: /root/.cache/pip/wheels/85/0d/12/77d219f3ebbb22dc22234b4d665886c0eace86a26eca0aa72b
Successfully built keybert
Installing collected packages: commonmark, rich, keybert
Successfully installed commonmark-0.9.1 keybert-0.7.0 rich-12.6.0

#importing the required packages
from google_play_scraper import Sort, reviews
import pandas as pd
from bertopic import BERTopic
from google.colab import files

#strings
#go to google play store: https://play.google.com/store/apps
#go to playstore app page
#copy the app id id={appid}
appURL = 'com.ubercab'

#scraping reviews from google play store
result, continuation_token = reviews(
    appURL, #app url
    lang='en', #language
    country='us', #country
    sort=Sort.NEWEST,
    count = 50000,
  #  filter_score_with = 1 # defaults to None(means all score)
)

result, _ = reviews(
    appURL,
    continuation_token=continuation_token
)

#putting everything into a dataframe
df = pd.DataFrame(result)

import pandas as pd

#df1 = pd.read_csv('_data/uberData.csv')
#df1

Discription

In this post, I will focus on cleaning and pre-processing my data into a format that is useful for analysis. This post will specifically focus on cleaning the content of the reviews, pre-processing them in python, then trying out different descriptives.

Data Cleaning

import re
import string
import nltk
nltk.download('punkt')
nltk.download('stopwords')
nltk.download("en")
nltk.download('wordnet')
nltk.download('omw-1.4')
nltk.download('words')
from nltk.corpus import stopwords
from nltk.classify import SklearnClassifier
from nltk.tokenize import word_tokenize
from nltk.stem.porter import PorterStemmer
from nltk.stem import WordNetLemmatizer
from wordcloud import WordCloud,STOPWORDS
import matplotlib.pyplot as plt

import spacy
nlp = spacy.load('en_core_web_sm')

[nltk_data] Downloading package punkt to /root/nltk_data...
[nltk_data]   Unzipping tokenizers/punkt.zip.
[nltk_data] Downloading package stopwords to /root/nltk_data...
[nltk_data]   Unzipping corpora/stopwords.zip.
[nltk_data] Error loading en: Package 'en' not found in index
[nltk_data] Downloading package wordnet to /root/nltk_data...
[nltk_data] Downloading package omw-1.4 to /root/nltk_data...
[nltk_data] Downloading package words to /root/nltk_data...
[nltk_data]   Unzipping corpora/words.zip.

def tokenize( data ):
    data1 = data.map(lambda x: word_tokenize(x))
    return data1

def removeStopwords( data ):
    stop_words = set(stopwords.words('english'))
    data1 = data.map(lambda x: [ w for w in x if w.lower() not in stop_words ])
    return data1

def lemmatize( data ):
    lemmatizer = WordNetLemmatizer()
    lemma_words = [ lemmatizer.lemmatize(w, pos='a') for w in data ]
    return lemma_words

# def stemming( data ):
#     ps = PorterStemmer()
#     stemmed_words = [ ps.stem(w) for w in data ]
#     return stemmed_words

def cleaning_data(data):
    data = data.map(lambda x: re.sub(r'\d+', ' ', x)) # Remove numbers
    data = data.map(lambda x: x.translate(x.maketrans('', '', string.punctuation))) # Remove Punctuation
    data = data.map(lambda x: x.strip()) # Remove white spaces
    data = tokenize( data )
    data = removeStopwords( data )
    data = data.map(lambda x: [w for w in x if w.isalpha()]) # Remove non alphabetic tokens
    data = data.map(lambda x: [w for w in x if len(w)>3 ]) #Removing small strings
    data = data.map(lambda x: ' '.join(x)) # turning back to string
    words = set(nltk.corpus.words.words())
    data = data.map(lambda x: [ w for w in nltk.wordpunct_tokenize(x) if w.lower() in words ]) # Remove non english words
    data = data.map(lambda x: ' '.join(x)) # turning back to string
    return data

df['content'] = lemmatize ( cleaning_data(df['content']) )
df = df.loc[df['content']!=''] #remove rows with empty content value

Word Cloud Plot

Word cloud is a technique for visualising frequent words in a text where the size of the words represents their frequency. Here we are trying to find common words in postive,negative and neutral reviews.

def scoreToSentiment(df):
    data = df['score']
    if data<=2:
        return "negative"
    elif data==3:
        return "neutral"
    else:
        return "positive"

df['sentiment'] = df.apply(lambda x : scoreToSentiment(x), axis = 1)

def wordcloudPlot(data, color = 'grey'):
    words = ' '.join(data)
    cleaned_word = " ".join([word for word in words.split()
                            if 'http' not in word
                                and not word.startswith('#')
                                and word != 'RT'
                            ])
    wordcloud = WordCloud(stopwords=STOPWORDS,
                      background_color=color,
                      width=2500,
                      height=2000
                     ).generate(cleaned_word)
    plt.figure(1,figsize=(13, 13))
    plt.imshow(wordcloud)
    plt.axis('off')
    plt.show()

print(wordcloudPlot(df['content']))

None

dfPostive = df.loc[df['sentiment']=='positive']
dfNeutral = df.loc[df['sentiment']=='neutral']
dfNegative = df.loc[df['sentiment']=='negative']

Positive Word Cloud

print(wordcloudPlot(dfPostive['content']))

None

Neutral Word Cloud

print(wordcloudPlot(dfNeutral['content']))

None

Negative Word Cloud

print(wordcloudPlot(dfNegative['content']))

None