Sentiment Analysis of Twitter Data

Abstract

The rapid expansion and acceptance of social media has opened doors into users opinions and perceptions that were never as accessible as they are with today's prevalence of mobile technology. Harvested data, analyzed for opinions and sentiment can provide powerful insight into a population. This research utilizes Twitter data due to its widespread global use, in order to examine the sentiment associated with users tweets. An approach utilizing Twitter #hashtags and Latent Dirichlet Allocation topic modeling were utilized to differentiate between tweet topics. A lexicographical dictionary was then utilized to classify sentiment. This method provides a framework for an analyst to ingest Twitter data, conduct an analysis and provide insight into the sentiment contained within the data.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2018
Accession Number
AD1056377

Entities

People

  • Evan L Munson

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • C4I
  • Cyber
  • Energy and Power Technologies
  • Space
  • Weapons Technologies

DTIC Thesaurus Topics

  • Air Force
  • Artificial Intelligence
  • Classification
  • Coding
  • Computers
  • Data Analysis
  • Data Mining
  • Department Of Defense
  • Dictionaries
  • Digital Data
  • Digital Information
  • Energetic Materials
  • Explosives
  • Geography
  • Governments
  • Identities
  • Information Science
  • Information Systems
  • Materials
  • Mathematics
  • Metadata
  • Network Science
  • Notation
  • Online Communications
  • Plastic Bonded Explosives
  • Plastic Explosives
  • Political Movements
  • Social Media
  • Social Networking Services

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Database Systems and Applications