Sentiment analysis on large-scale social media data is important to bridge the gaps between social media contents and real world activities including political election prediction, individual and public emotional status monitoring and analysis, and so on. Although textual sentiment analysis has been well studied based on platforms such as Twitter and Instagram, analysis of the role of extensive emoji uses in sentiment analysis remains light. In this paper, we propose a novel scheme for Twitter sentiment analysis with extra attention on emojis.We first learn bi-sense emoji embeddings under positive and negative sentimental tweets individually, and then train a sentiment classifier by attending on these bi-sense emoji embeddings with an attention-based long short-term memory network (LSTM). Our experiments show that the bi-sense embedding is effective for extracting sentiment-aware embeddings of emojis and outperforms the state-of-the-art models. We also visualize the attentions to show that the bi-sense emoji embedding provides better guidance on the attention mechanism to obtain a more robust understanding of the semantics and sentiments.
Incorporating Sentiment Analysis with Epistemic Network Analysis to Enhance Discourse Analysis of Twitter Data
While there has been much growth in the use of microblogging platforms (e.g., Twitter) to share information on a range of topics, researchers struggle
to analyze the large volumes of data produced on such platforms. Established methods such as Sentiment Analysis (SA) have been criticized over their inaccuracy
and limited analytical depth. In this exploratory methodological paper, we propose
a combination of SA with Epistemic Network Analysis (ENA) as an alternative
approach for providing richer qualitative and quantitative insights into Twitter
discourse. We illustrate the application and potential use of these approaches by
visualizing the differences between tweets directed or discussing Democrats and
Republicans after the COVID-19 Stimulus Package announcement in the US. SA
was integrated into ENA models in two ways: as a part of the blocking variable
and as a set of codes. Our results suggest that incorporating SA into ENA allowed
for a better understanding of how groups viewed the components of the stimulus
issue by splitting them by sentiment and enabled a meaningful inclusion of data
with singular subject focus into the ENA models.
- Editors:
- Ruis, Andrew R.; Lee, Seung B.
- Award ID(s):
- 1661036
- Publication Date:
- NSF-PAR ID:
- 10248622
- Journal Name:
- Advances in Quantitative Ethnography: Second International Conference, ICQE 2020, Malibu, CA, USA, February 1-3, 2021, Proceedings
- Page Range or eLocation-ID:
- 375-389
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Publisher's Version of Record
- https://doi.org/https://doi.org/10.1007/978-3-030-67788-6_26
