Abstract
Twitter Sentiment Analysis shows several challenges due to the platform’s features (e.g., short messages, colloquial style, etc.). People want to express their ideas related to personality, events, or breaking news. Social media is one of the fastest ways to express opinions, and research directions are developed to analyze the polarity of written messages. Very important for this domain is how you process the data and define features that can mine valuable information from textual inputs. Exploring various features and combining them can increase the quality of the entire methodology. Hence, a new system is designed to build a lexicon-based feature for detecting the polarity of a tweet. Therefore, messages posted by a user on Twitter are enhanced with a sentiment indicator provided by a lexicon. Then, the new model will be used by a classification algorithm. The numerical experiments are developed on several different datasets in terms of size and topics. The results highlight that the defined feature outperforms other lexicon-based features from literature. Moreover, the experiments based on the sentiment indicator produce better performance values than the traditional approaches that use the original tweet without additional features.
Citare
@Inproceedings{Limboi2022ALF,
author = {Sergiu Limboi and L. Dioşan},
booktitle = {International Conference on Computational Photography},
title = {A Lexicon-based Feature for Twitter Sentiment Analysis},
year = {2022}
}