Sentiment Analysis: YouTube comments on NASA Mars landing

Few days back I was astonished to learn that NASA was landing on the MARS. Because I cannot land on mars I did what I can do; the data analysis.

Download the csv file from Kaggle and start your Jupyter notebook.

Let’s Move straight to coding:

import pandas as pd
df=pd.read_csv('landing.csv')
print(df)

Data frame(df) is a table or a two dimensional array like structure in which data from CSV file is stored.

Output:

Output:
Unnamed: 0      time                            comment
0               0     0:03                   was uppp everyone
1               1     0:05                          this is it
2               2     0:07                            bobchamp
3               3     0:07          Omg Im so excited for this
4               4     0:07                         french here
...           ...       ...                                ...
16125       16125  2:11:41                   R I P David Bowie
16126       16126  2:11:41   _perfect_effect_ nice to meet you
16127       16127  2:11:41                        its finished
16128       16128  2:11:42                            Bye chat
16129       16129  2:11:43        Congratulations team Go NASA

[16130 rows x 3 columns]
import nltk
nltk.download("vader_lexicon")
from nltk.sentiment.vader import SentimentIntensityAnalyzer
vader = SentimentIntensityAnalyzer()

importing NLTK library and using vader function for sentiment analysis.

df["scores"] = df["comment"].apply(lambda comment: vader.polarity_scores(comment))
df.head()
df["compound"]=df["scores"].apply(lambda score_dict:score_dict["compound"])
df["sentiment"]=df["compound"].apply(lambda c:"pos" if c>=0 else "neg")
print(df)

created a new column called “scores” to assign sentiment score to the YouTube comment.

Output:

Unnamed: 0      time                            comment  \
0               0     0:03                   was uppp everyone   
1               1     0:05                          this is it   
2               2     0:07                            bobchamp   
3               3     0:07          Omg Im so excited for this   
4               4     0:07                         french here   
...           ...       ...                                ...   
16125       16125  2:11:41                   R I P David Bowie   
16126       16126  2:11:41   _perfect_effect_ nice to meet you   
16127       16127  2:11:41                        its finished   
16128       16128  2:11:42                            Bye chat   
16129       16129  2:11:43        Congratulations team Go NASA   

                                                  scores  compound sentiment  
0      {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...    0.0000       pos  
1      {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...    0.0000       pos  
2      {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...    0.0000       pos  
3      {'neg': 0.0, 'neu': 0.616, 'pos': 0.384, 'comp...    0.4795       pos  
4      {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...    0.0000       pos  
...                                                  ...       ...       ...  
16125  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...    0.0000       pos  
16126  {'neg': 0.0, 'neu': 0.588, 'pos': 0.412, 'comp...    0.4215       pos  
16127  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...    0.0000       pos  
16128  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...    0.0000       pos  
16129  {'neg': 0.0, 'neu': 0.435, 'pos': 0.565, 'comp...    0.5994       pos

3 new columns were added, “scores”, “compound”, “sentiment” for the analysis of the comments were made immediately after NASA perseverance landed on Mars.