sis 19(22): e5

Research Article

Bayesian Sentiment Analytics for Emerging Trends in Unstructured Data Streams

Download1084 downloads
Cite
BibTeX Plain Text
  • @ARTICLE{10.4108/eai.13-7-2018.159355,
    author={Najam us Sahar and Muhammad Sohail Irshad and Muhammad Adnan Khan},
    title={Bayesian Sentiment Analytics for Emerging Trends in Unstructured Data Streams},
    journal={EAI Endorsed Transactions on Scalable Information Systems},
    volume={6},
    number={22},
    publisher={EAI},
    journal_a={SIS},
    year={2019},
    month={7},
    keywords={Na\~{n}ve Bayes, Bag of Words, Bag of Nouns, Term Frequency, Inverse Document Frequency},
    doi={10.4108/eai.13-7-2018.159355}
}
  • Najam us Sahar
    Muhammad Sohail Irshad
    Muhammad Adnan Khan
    Year: 2019
    Bayesian Sentiment Analytics for Emerging Trends in Unstructured Data Streams
    SIS
    EAI
    DOI: 10.4108/eai.13-7-2018.159355
Najam us Sahar1, Muhammad Sohail Irshad1, Muhammad Adnan Khan2,*
  • 1: Department of Computer Science, Minhaj University Lahore.
  • 2: School of Computer Science, National College of Business Administration and Economics Lahore, Pakistan.
*Contact email: madnankhan@ncbae.edu.pk

Abstract

Today the computational study of people’s opinion expressed in free form written text is called the field of sentiment analysis and opinion mining. Various research areas such as Natural Language Processing, Data Mining, Text Mining lie in field of Sentiment Analysis and is also becoming major part of importance to organizations because of online commerce is included in their operational strategy. Due to excess of user’s comments, feedback on web there is a need to analyze the user generated text. This research focuses on aspect level sentiment analysis in which identification of aspects and their related sentiments is being done. Opinion analysis helps to identify the polarity of the text and feature extraction. This study is being done to provide an effective and efficient framework to calculate the sentiments of written text by using Naïve Bayes approach. For sentiment analysis dataset of 1060 reviews of different restaurants from online website TripAdvisor.com is being used. The outcome achieved good accuracy 80.833 percent.

Keywords
Naïve Bayes, Bag of Words, Bag of Nouns, Term Frequency, Inverse Document Frequency
Received
2019-05-06
Accepted
2019-05-28
Published
2019-07-04
Publisher
EAI
http://dx.doi.org/10.4108/eai.13-7-2018.159355

Copyright © 2019 Najam us Sahar et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.