Unsupervised Sentiment Classification of Twitter Data using Emoticons

Hiremath, S.V.; Manjula, S H; Venugopal, K R

doi:10.1109/esci50559.2021.9397026

Cited by 8 publications

(1 citation statement)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition to Japanese texts, research has been conducted on texts containing pictograms in Chinese, Turkish, Arabic, and other languages. There are many other studies that use emojis as a cue for emotion estimation, and many have also evaluated the effectiveness of emojis experimentally [15][16][17][18][19][20][21][22]. The number of studies on emoticons is small compared to those on pictograms.…”

Section: Cherbonnier Et Al [12] Compared the Recognition Of Human Emo...mentioning

confidence: 99%

Emotion Estimation Method Based on Emoticon Image Features and Distributed Representations of Sentences

et al. 2022

View full text Add to dashboard Cite

This paper proposes an emotion recognition method for tweets containing emoticons using their emoticon image and language features. Some of the existing methods register emoticons and their facial expression categories in a dictionary and use them, while other methods recognize emoticon facial expressions based on the various elements of the emoticons. However, highly accurate emotion recognition cannot be performed unless the recognition is based on a combination of the features of sentences and emoticons. Therefore, we propose a model that recognizes emotions by extracting the shape features of emoticons from their image data and applying the feature vector input that combines the image features with features extracted from the text of the tweets. Based on evaluation experiments, the proposed method is confirmed to achieve high accuracy and shown to be more effective than methods that use text features only.

show abstract