Design and analysis of a large-scale covid-19 tweets dataset

  • How to do sentiment analysis on tweets?

    Performing sentiment analysis on Twitter data involves five steps:

    1. Gather relevant Twitter data
    2. Clean your data using pre-processing techniques
    3. Create a sentiment analysis machine learning model
    4. Analyze your Twitter data using your sentiment analysis model
    5. Visualize the results of your Twitter sentiment analysis

  • How to do sentiment analysis on tweets?

    A Twitter sentiment analysis identifies negative, positive, or neutral emotions within the text of a tweet.
    It is a text analysis using natural language processing (NLP) and machine learning..

  • How to do sentiment analysis on Twitter data?

    A Twitter sentiment analysis identifies negative, positive, or neutral emotions within the text of a tweet.
    It is a text analysis using natural language processing (NLP) and machine learning..

  • What is the methodology of Twitter sentiment analysis?

    Sentiment analysis of Twitter data involves Opinion Mining to analyze the psychological intent in a tweet – positive, negative, or neutral.
    Then, based on the patterns identified during text mining, it predicts the subsequent thread of texts..

  • What is the scope of Twitter sentiment analysis?

    It will measure how positive or negative a report or hashtag is.
    This is the “Sentiment Score” and it is given in a score from 1 to 100.
    This way, a report with a Sentiment Score of 90 will be very positive whereas a report with a score of 10 will be quite negative..

  • What technology is used in Twitter sentiment analysis?

    A Twitter sentiment analysis identifies negative, positive, or neutral emotions within the text of a tweet.
    It is a text analysis using natural language processing (NLP) and machine learning..

  • What technology is used in Twitter sentiment analysis?

    Twitter Sentimental Analysis is used to identify as well as classify the sentiments that are expressed in the text source.
    Logistic Regression, SVM, and Naive Bayes are some of the ML algorithms that can be used for Twitter Sentimental Analysis..

  • Twitter Sentimental Analysis is used to identify as well as classify the sentiments that are expressed in the text source.
    Logistic Regression, SVM, and Naive Bayes are some of the ML algorithms that can be used for Twitter Sentimental Analysis.

Infrastructure

The collection of tweets is a small portion of the dataset design

The Sentiment Scores

The dataset has two columns: Tweet ID and Sentiment score. During the project’s inception

Filtering Geo-Tagged Tweets

Geotagging is the process of placing location information in a tweet

Dataset Releases

Twitter’s content redistribution policy restricts the sharing of tweet information other than tweet IDs, Direct Message IDs and/or User IDs

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