Sentiment Analysis by Fusing Text and Location Features of Geo-Tagged Tweets


Ting, Choo Yee and Ho, Chiung Ching and Lim, Wei Lun (2020) Sentiment Analysis by Fusing Text and Location Features of Geo-Tagged Tweets. IEEE Access, 8. pp. 181014-181027. ISSN 2169-3536

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Twitter sentiment analysis provides valuable feedback from public emotion concerning certain events or products. Current research has been focused on obtaining sentiment features from vectorized lexical and syntactic feature from tweets, without further context. In this paper, we demonstrated how vectorized location information could be combined with word embeddings to produce a hybrid representation, which has resulted in an improvement on a tweet sentiment classification task. The location information of the geo-tagged tweets provided further context, which was useful for a sentiment classification task. The tweets investigated contained a set of geo-tagged tweets. The word embeddings of these tweets were combined with the geo-tagged tweets' vectorized location features to form a sentiment feature set of geo-tagged tweets. The sentiment feature set was incorporated into a convolution neural network and a bi-directional long short-term memory network for the tasks of training and predicting of sentiment classification labels. This hybrid representation is compared with the baseline GloVe model through a few experiments, and the results have shown that the incorporation of vectorized location information has resulted in improvement of the accuracy for the twitter sentiment classification task.

Item Type: Article
Uncontrolled Keywords: Pattern recognition systems
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800-8360 Electronics > TK7885-7895 Computer engineering. Computer hardware
Divisions: Faculty of Computing and Informatics (FCI)
Depositing User: Ms Suzilawati Abu Samah
Date Deposited: 22 Oct 2021 02:40
Last Modified: 22 Oct 2021 02:40


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