please help me write a code in python using jupyter notebook and NLTK $**$Objective:$**$

In this lab tutorial, you will learn how to perform social media text analytics using Python. You will use the Natural Language Toolkit $($NLTK$)$ to extract insights from social media text data.

$**$Step $1$: Setup$**$

$-$ Make sure you have Python, NLTK$,$ and Jupyter Notebook installed. If not, you can install NLTK using $`$pip install nltk$`.$

$-$ Start a Jupyter Notebook by running $`$jupyter notebook$`$ in your terminal.

$**$Step $2$: Data Collection$**$

For this tutorial, we'll use a sample dataset of social media text. You can also scrape data from social media platforms using APIs or other methods.

$```$python

import pandas as pd

# Load sample social media data

data $=$ pd$.$read$\_$csv$("$social$\_$media$\_$data.csv$")$

$```$

$**$Step $3$: Data Preprocessing$**$

Before performing text analysis, clean and preprocess the data. You can perform the following tasks:

$-$ Tokenization $($splitting text into words or tokens$)$

$-$ Removing punctuation and stopwords

$-$ Lowercasing

$```$python

import nltk

from nltk$.$corpus import stopwords

from nltk$.$tokenize import word$\_$tokenize

nltk$.$download$("$stopwords$")$

nltk$.$download$("$punkt$")$

# Tokenization and preprocessing

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

tokens $=$ word$\_$tokenize$($text$)$

tokens $=[$token$.$lower$()$ for token in tokens if token.isalpha$()]$

tokens $=[$token for token in tokens if token not in set$($stopwords$.$words$(\text{'}$english$\text{'}))]$

return tokens

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

$```$

$**$Step $4$: Text Analysis$**$

Now, let's perform some basic text analysis tasks:

$-**$Word Frequency Analysis$**$: Count the frequency of words in the dataset.

$-**$Sentiment Analysis$**$: Analyze the sentiment of the text $($positive$,$ negative, neutral$).$

$```$python

from nltk$.$probability import FreqDist

from nltk$.$sentiment$.$vader import SentimentIntensityAnalyzer

# Word frequency analysis

all$\_$words $=[$word for tokens in data$[\text{'}$text$\text{'}]$ for word in tokens$]$

word$\_$freq $=$ FreqDist$($all$\_$words$)$

# Sentiment analysis

sid $=$ SentimentIntensityAnalyzer$()$

def get$\_$sentiment$($text$)$:

sentiment $=$ sid.polarity$\_$scores$($text$)$

if sentiment$[\text{'}$compound$\text{'}]>=0\mathrm{.}05$:

return 'positive'

elif sentiment$[\text{'}$compound$\text{'}]<=-0\mathrm{.}05$:

return 'negative'

else:

return 'neutral'

data$[\text{'}$sentiment$\text{'}]=$ data$[\text{'}$text$\text{'}].$apply$($get$\_$sentiment$)$

$```$

$**$Step $5$: Data Visualization$**$

To visualize the results, you can create word clouds, histograms, and sentiment plots.

$```$python

import matplotlib.pyplot as plt

from wordcloud import WordCloud

# Word cloud

wordcloud $=$ WordCloud$($width$=800,$ height$=400,$ background$\_$color$=$"white"$).$generate$\_$from$\_$frequencies$($word$\_$freq$)$

plt$.$figure$($figsize$=(10,5))$

plt$.$imshow$($wordcloud$,$ interpolation$=$"bilinear"$)$

plt$.$axis$("$off$")$

plt$.$show$()$

# Sentiment plot

sentiment$\_$counts $=$ data$[\text{'}$sentiment$\text{'}].$value$\_$counts$()$

sentiment$\_$counts.plot$($kind$=$'bar', color$=[\text{'}$green$\text{'},$ 'red', 'blue'$])$

plt$.$title$(\text{'}$Sentiment Analysis'$)$

plt$.$xlabel$(\text{'}$Sentiment$\text{'})$

plt$.$ylabel$(\text{'}$Count$\text{'})$

plt$.$show$()$

$```$

$**$Step $6$: Interpretation$**$

Discuss the results with your students. Interpret the word frequency, sentiment analysis, and any other insights gained from the analysis. Encourage them to think about the practical applications of these insights in a real$-$world social media analytics scenario.