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The aim of the project is to build a model which would predict the opinion of the people in tweets under particular # In this case we have used the omicron variant of COVID to see the world's opnion about it

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sentimental-analysis-of-omicron's Introduction

CoviResp

Video demonstration :

https://fb.watch/dzcR7kVEoL/

GOAL

The aim of the project is to build a model which would predict the opinion of the people in tweets under particular # In this case we have used the omicron variant of COVID to see the world's opnion about it

DATASET

The dataset was created by fetching the tweets from twitter using tweepy and converted to dataframe using Pandas library

DESCRIPTION

In this project, we compare the sentiments of the tweets either positive,negative, or neutral opinion from vader_lexicon and tried to calculate the score of each sentiments

WHAT I HAD DONE

Steps followed:

Created a twitter developer account and fetched my consumer_key, consumer_secret, access_token, access_token_secret

Connecting to Twitter with API

Gathering the tweets regarding OMICRON

Setting up the dataset

Then performed Data cleaning :Removing the stopword and handling unnecessary wordse

Then performed Visualising of the tweets using wordcloud

Then used vader_lexicon (Valence Aware Dictionary and sEntiment Reasoner) to analyse positive,negative and neutral tweets

Then Calculated the Sentiment Score

MODELS USED

To be specific we have used a tookit Natural Language Toolkit from python to which provides easy-to-use interfaces to over 50 corpora and lexical resources such as vader_lexicon, SentimentIntensityAnalyzer, SnowballStemmer , stopwords removal to analyse the tweets and calculate the Sentiment Score

##LIBRARIES NEEDED