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Sentiment analysis of social media content using N-Gram graphs

Published: 30 November 2011 Publication History

Abstract

Sentiment Analysis over Social Media facilitates the extraction of useful conclusions about the average public opinion on a variety of topics, but poses serious technical challenges. This is because of the sparse, noisy, multilingual content that is posted on-line by Social Media users. In this paper, we introduce a novel method for capturing textual patterns that inherently supports this challenging type of content. In essence, it creates a graph whose nodes correspond to the character n-grams of a document, while its weighted edges denote the average distance between them. Multiple documents of the same polarity can be aggregated into a polarity class graph, which can be compared with individual documents in order to identify the category of their sentiment. To evaluate our approach, we conducted large scale experiments on a real-world data set stemming from a snapshot of Twitter activity. The outcomes of our evaluation indicate significant improvements over other the methods typically used in this context, not only with respect to effectiveness, but also to efficiency.

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  • (2024)Estimating Telecommuting Rates in the USA Using Twitter Sentiment AnalysisData Science for Transportation10.1007/s42421-024-00114-06:3Online publication date: 29-Oct-2024
  • (2023)Understanding Quality of Products from Customers’ Attitude Using Advanced Machine Learning MethodsComputers10.3390/computers1203004912:3(49)Online publication date: 22-Feb-2023
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      cover image ACM Conferences
      WSM '11: Proceedings of the 3rd ACM SIGMM international workshop on Social media
      November 2011
      74 pages
      ISBN:9781450309899
      DOI:10.1145/2072609
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Publication History

      Published: 30 November 2011

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      Author Tags

      1. n-gram graphs
      2. polarity classification
      3. social media

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      MM '11
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      MM '11: ACM Multimedia Conference
      November 30, 2011
      Arizona, Scottsdale, USA

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      Cited By

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      • (2024)Global Perspective on EMR and eHealthInternational Journal of Intelligent Information Technologies10.4018/IJIIT.34304620:1(1-29)Online publication date: 17-Sep-2024
      • (2024)Estimating Telecommuting Rates in the USA Using Twitter Sentiment AnalysisData Science for Transportation10.1007/s42421-024-00114-06:3Online publication date: 29-Oct-2024
      • (2023)Understanding Quality of Products from Customers’ Attitude Using Advanced Machine Learning MethodsComputers10.3390/computers1203004912:3(49)Online publication date: 22-Feb-2023
      • (2023)Prescriptive graph analytics on the digital transformation in healthcare through user-generated contentAnnals of Operations Research10.1007/s10479-023-05495-zOnline publication date: 5-Jul-2023
      • (2023)A systematic review of social network sentiment analysis with comparative study of ensemble-based techniquesArtificial Intelligence Review10.1007/s10462-023-10472-w56:11(13407-13461)Online publication date: 12-Apr-2023
      • (2022)KENGIC: KEyword-driven and N-Gram Graph based Image Captioning2022 International Conference on Digital Image Computing: Techniques and Applications (DICTA)10.1109/DICTA56598.2022.10034584(1-8)Online publication date: 30-Nov-2022
      • (2022)An Enhanced Exploration of Sentimental Analysis in Health CareWireless Personal Communications10.1007/s11277-022-09981-8128:2(901-922)Online publication date: 19-Oct-2022
      • (2022)Text Analytics Using Graph TheoryInformation Retrieval and Natural Language Processing10.1007/978-981-16-9995-5_6(117-134)Online publication date: 23-Feb-2022
      • (2022)Social Versus Physical Distancing: Analysis of Public Health Messages at the Start of COVID-19 Outbreak in Malaysia Using Natural Language ProcessingProceedings of the 8th International Conference on Computational Science and Technology10.1007/978-981-16-8515-6_44(577-589)Online publication date: 26-Mar-2022
      • (2021)Non-Verbal behaviors analysis of healthcare professionals engaged with a Virtual-PatientCompanion Publication of the 2021 International Conference on Multimodal Interaction10.1145/3461615.3485442(353-361)Online publication date: 18-Oct-2021
      • Show More Cited By

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