A Clustering Analysis of Tweet Length and its Relation to Sentiment
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This document outlines a research project analyzing the relationship between tweet length and sentiment scores using data from Twitter, which has over 645 million users and generates 58 million tweets daily. The project aims to capture Twitter data, build a custom sentiment dictionary, process the tweets, create a dataset, and perform clustering analysis. The focus is on identifying subjective information such as attitudes and emotions within tweets.
A Clustering Analysis of Tweet Length and its Relation to Sentiment
- 1. A CLUSTERING ANALYSIS OF TWEET LENGTH AND ITS RELATION TO SENTIMENT
- 2. Research Project Matthew Mayo School of Computer Science Columbus State University Columbus, GA 31907 CPSC 6185 Intelligent Agents Dr. Rania Hodhod
- 3. Twitter • Popular microblogging web service • 140 character per message (tweet) limit • Started in 2006, over 645 million users today* • 58 million tweets per day* • 9,000 tweets per minute* * Source: www.statisticbrain.com/twitter-statistics
- 4. Sentiment Analysis • Identifying, extracting & processing subjective information from source material • Subjective information includes attitudes, emotions & opinions • Appropriate for binary classification (positive vs. negative, good vs. bad, etc.) • Useful for movie reviews, political election opinions, etc.
- 5. Project Aim Interested in exploring the relationship between: • Length of tweet (number of characters) AND • Sentiment score of tweet
- 6. Problem Description The research project tasks: 1. Capture Twitter data 2. Build custom sentiment dictionary 3. Process tweets 4. Create dataset 5. Cluster tweet data