Book ChapterDOI
Novel Text Preprocessing Framework for Sentiment Analysis
C. S. Pavan Kumar,L. D. Dhinesh Babu +1 more
- pp 309-317
Reads0
Chats0
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
More filters
Proceedings ArticleDOI
Sentiment analysis of preschool teachers’ perceptions on ICT use for young children
TL;DR: This paper summarizes the findings using sentiment analysis as well as comparing it to the quantitative data obtained from the survey, where most teachers agreed upon the benefits of ICT use and conclude more positive sentiment polarity.
Journal ArticleDOI
Fuzzy based feature engineering architecture for sentiment analysis of medical discussion over online social networks
TL;DR: A novel Feature engineering architecture that involves feature-split for sentiment analysis of medical chatter over online social networks with the pipeline is proposed that can be used on any Machine Learning model.
Journal ArticleDOI
Adversarial attacks on a lexical sentiment analysis classifier
Gildásio Antonio de Oliveira Júnior,Rafael Timóteo de Sousa Júnior,Robson de Oliveira Albuquerque,Robson de Oliveira Albuquerque,Luis Javier García Villalba +4 more
TL;DR: In this article, the authors present adversarial attacks related to a lexical natural language classifier, which is used to calculate the sentiment of collected data as posted by users in various social media applications.
Exploring the Relationship Between Vocabulary Scaling and Algorithmic Performance in Text Classification for Large Datasets
TL;DR: This article explored the relationship between model quality and runtime for text analysis by looking at the effect that current techniques in vocabulary reduction have on algorithmic runtime and comparing that with their effect on model quality.
References
More filters
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.
Related Papers (5)
Analysis of Twitter Specific Preprocessing Technique for Tweets
Dharini Ramachandran,R. Parvathi +1 more