Book ChapterDOI
Novel Text Preprocessing Framework for Sentiment Analysis
C. S. Pavan Kumar,L. D. Dhinesh Babu +1 more
- pp 309-317
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TLDR
A text preprocessing model for sentiment analysis (SA) over twitter posts with the help of Natural Language processing (NLP) techniques is proposed to reduce the dimensionality problem and execution time.Abstract:
Aim of this article is to propose a text preprocessing model for sentiment analysis (SA) over twitter posts with the help of Natural Language processing (NLP) techniques. Discussions and investments on health-related chatter in social media keep on increasing day by day. Capturing the actual intention of the tweeps (twitter users) is challenging. Twitter posts consist of Text. It needs to be cleaned before analyzing and we should reduce the dimensionality problem and execution time. Text preprocessing plays an important role in analyzing health-related tweets. We gained 5.4% more accurate results after performing text preprocessing and overall accuracy of 84.85% after classifying the tweets using LASSO approach.read more
Citations
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Journal ArticleDOI
Evolving dictionary based sentiment scoring framework for patient authored text
TL;DR: The results prove that the proposed approach is an effective strategy for sentiment analysis over patient authored text which helps in improving the classification accuracy.
Proceedings ArticleDOI
An Analysis on Use of Deep Learning and Lexical-Semantic Based Sentiment Analysis Method on Twitter Data to Understand the Demographic Trend of Telemedicine
TL;DR: The finding suggests that lexical and semantic-based methods for sentiment prediction offer better accuracy than Deep Learning methods; when a large enough and evenly distributed training dataset is not available.
Book ChapterDOI
Natural Language Processing (NLP): An Introduction
Roman Egger,Enes Gokce +1 more
TL;DR: In this paper , the authors provide the reader with the basics of NLP as well as present the text pre-processing procedure in detail, which can expand the text mining potential enormously, leading to deeper insights, a better understanding of social phenomena, and a better basis for decision-making.
Proceedings ArticleDOI
Semantic Textual Similarity of Sentences with Emojis
TL;DR: The amount of semantic information lost by discounting emojis is qualitatively ascertained, as well as a mechanism of accounting for emojiis in a semantic task is shown.
Journal ArticleDOI
Tweet-scan-post: a system for analysis of sensitive private data disclosure in online social media
TL;DR: In this article, a Tweet-Scan-Post (TSP) framework is proposed to identify the presence of sensitive private data (SPD) in user's posts under personal, professional, and health domains.
References
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News use across social media platforms 2017
TL;DR: For instance, a survey conducted by the Pew Research Center found that a majority of adults in the United States access their news on social media, with 18% doing so often as mentioned in this paper.
Journal ArticleDOI
The Role of Text Pre-processing in Sentiment Analysis
Emma Haddi,Xiaohui Liu,Yong Shi +2 more
TL;DR: The role of text pre-processing in sentiment analysis is explored, and it is demonstrated that with appropriate feature selection and representation, sentiment analysis accuracies using support vector machines (SVM) in this area may be significantly improved.
News use across social media platforms 2016
TL;DR: For instance, a survey conducted by the Pew Research Center found that a majority of adults in the United States access their news on social media, with 18% doing so often as mentioned in this paper.
Journal ArticleDOI
Comparison Research on Text Pre-processing Methods on Twitter Sentiment Analysis
Zhao Jianqiang,Gui Xiaolin +1 more
TL;DR: The experiments show that the accuracy and F1-measure of Twitter sentiment classification classifier are improved when using the pre-processing methods of expanding acronyms and replacing negation, but barely changes when removing URLs, removing numbers or stop words.
Proceedings Article
How Noisy Social Media Text, How Diffrnt Social Media Sources?
TL;DR: This work investigates just how linguistically noisy or otherwise text in social media text is over a range of social media sources, in the form of YouTube comments, Twitter posts, web user forum posts, blog posts and Wikipedia, which is compared to a reference corpus of edited English text.
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