Impact analysis of adverbs for sentiment classification on Twitter product reviews

Sajjad Haider, Muhammad Tanvir Afzal, Muhammad Asif, Hermann Maurer, Awais Ahmad, Abdelrahman Abuarqoub

Research output: Contribution to journalArticleResearchpeer-review

Abstract

Social networking websites such as Twitter provide a platform where users share their opinions about different news, events, and products. A recent research has identified that 81% of users search online first before purchasing products. Reviews are written in natural language and needs sentiment analysis for opinion extraction. Various approaches have been proposed to perform sentiment classification based on polarity bearing words in reviews such as noun, verb, adverb, and an adjective. Prior researchers have also identified the role of an adverb as a feature. However, impact analysis of adverb forms, are not yet studied and remains an open research area. This study focused on the following tasks: (1) impact of different forms of adverbs that are not studied for sentiment classification; (2) analysis of possible combinations of eight forms that are 255. The different forms are Adverb (RA), Degree Adverbs (RG), Degree Comparative Adverbs (RGR), General Adverbs (RR), General Comparative Adverbs (RRR), Locative Adverbs (RL), Prep. Adverb (RP), and Adverbs of time (RT); (3) comparison with benchmark dataset. Dataset of 5513 tweets is used to evaluate the idea. The findings of this work show that RRR and RR are important polarities bearing words for neutral opinions, RL for positive, and RP for negative opinions.

Original languageEnglish
JournalConcurrency and Computation
Early online date29 Aug 2018
DOIs
Publication statusE-pub ahead of print - 29 Aug 2018

Fingerprint

Bearings (structural)
Polarity
Purchasing
Websites
Relative Growth Rate
Sentiment Analysis
Social Networking
Natural Language
Benchmark
Form
Review
Evaluate

Keywords

  • adverbs sentiment classification
  • polarity classification
  • sentiment analysis
  • Twitter product review
  • Twitter sentiment analysis

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Computer Science Applications
  • Computer Networks and Communications
  • Computational Theory and Mathematics

Cite this

Impact analysis of adverbs for sentiment classification on Twitter product reviews. / Haider, Sajjad; Tanvir Afzal, Muhammad; Asif, Muhammad; Maurer, Hermann; Ahmad, Awais; Abuarqoub, Abdelrahman.

In: Concurrency and Computation, 29.08.2018.

Research output: Contribution to journalArticleResearchpeer-review

Haider, Sajjad ; Tanvir Afzal, Muhammad ; Asif, Muhammad ; Maurer, Hermann ; Ahmad, Awais ; Abuarqoub, Abdelrahman. / Impact analysis of adverbs for sentiment classification on Twitter product reviews. In: Concurrency and Computation. 2018.
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