Research Article
Bayesian Sentiment Analytics for Emerging Trends in Unstructured Data Streams
@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
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.
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.
