Novel Text Preprocessing Framework for Sentiment Analysis
TL;DR: 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.
Cites methods from "Novel Text Preprocessing Framework ..."
...Additionally, a new, improved method for tweet text cleaning can be implemented, which cleans the tweet in a way that the original sentiment stays intact ....
Cites background from "Novel Text Preprocessing Framework ..."
...3383758 More often than not, semantic classification tasks treat emojis as noise and remove them from the dataset in the pre-processing stage ....