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SentiLangN: A Language-Neutral Graph-Based Approach for Sentiment Analysis in Microblogging Data

Authors:

Muhammad Abulaish,

Mohammad Rahimi,

Habeebullah Ebrahemi,

Amit SahAuthors Info & Claims

WI '19: IEEE/WIC/ACM International Conference on Web Intelligence

Pages 461 - 465

https://doi.org/10.1145/3350546.3352568

Published: 14 October 2019 Publication History

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Abstract

In this paper, we present a language-neutral graph-based sentiment analysis approach, SentiLangN, which uses character n-gram graph for modelling textual data to handle language-neutral unstructured expressions and noisy data. Since ordering and positioning of characters and words in a document plays a vital role in content analysis, the SentiLangN employs the longest common subsequence and degree similarity to capture inherent semantics of the textual data.

SentiLangN introduces averaged character n-gram graph model and an application of long-short-term memory (LSTM) approach for sentiment analysis. The performance of SentiLangN is evaluated over real Twitter dataset, and it performs better than the individual n-gram graph models and traditional machine learning algorithms like C4.5. It is also compared with one of the state-of-the-art methods and performs significantly better.

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Cited By

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Malyshenko KMalyshenko VAnashkina MAnashkin D(2024)Implementation of Text Mining in Socio-Economic ResearchInternational Journal of Business Data Communications and Networking10.4018/IJBDCN.34126319:1(1-21)Online publication date: 27-Mar-2024
https://doi.org/10.4018/IJBDCN.341263
Wasi NAbulaish M(2020)Document-Level Sentiment Analysis through Incorporating Prior Domain Knowledge into Logistic Regression2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)10.1109/WIIAT50758.2020.00148(969-974)Online publication date: Dec-2020
https://doi.org/10.1109/WIIAT50758.2020.00148

Index Terms

SentiLangN: A Language-Neutral Graph-Based Approach for Sentiment Analysis in Microblogging Data
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
  2. Machine learning
2. Information systems
  1. Information systems applications

Index terms have been assigned to the content through auto-classification.

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WI '19: IEEE/WIC/ACM International Conference on Web Intelligence

October 2019

507 pages

ISBN:9781450369343

DOI:10.1145/3350546

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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Published: 14 October 2019

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WI '19: IEEE/WIC/ACM International Conference on Web Intelligence

October 14 - 17, 2019

Thessaloniki, Greece

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Malyshenko KMalyshenko VAnashkina MAnashkin D(2024)Implementation of Text Mining in Socio-Economic ResearchInternational Journal of Business Data Communications and Networking10.4018/IJBDCN.34126319:1(1-21)Online publication date: 27-Mar-2024
https://doi.org/10.4018/IJBDCN.341263
Wasi NAbulaish M(2020)Document-Level Sentiment Analysis through Incorporating Prior Domain Knowledge into Logistic Regression2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)10.1109/WIIAT50758.2020.00148(969-974)Online publication date: Dec-2020
https://doi.org/10.1109/WIIAT50758.2020.00148

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Building an Arabic Sentiment Lexicon Using Semi-supervised Learning

Investigating Metaphorical Language in Sentiment Analysis: A Sense-to-Sentiment Perspective

A bilingual approach for conducting Chinese and English social media sentiment analysis

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