# Twitter Sentiment Analysis via Bi-sense Emoji Embedding and Attention-based LSTM

arXiv: https://arxiv.org/abs/1807.07961 · HTML: https://mlsanity.com/paper/1807.07961/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2018.

## Built on by (most cited first, 17 of 17)

- [General Debiasing for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2307.10511/) (2023)
- [A `Sourceful' Twist: Emoji Prediction Based on Sentiment, Hashtags and Application Source](https://mlsanity.com/paper/2103.07833/) (2021)
- [Contrastive Learning of Sociopragmatic Meaning in Social Media](https://mlsanity.com/paper/2203.07648/) (2022)
- [Emoji Driven Crypto Assets Market Reactions](https://mlsanity.com/paper/2402.10481/) (2024)
- [From Adoption to Adaption: Tracing the Diffusion of New Emojis on Twitter](https://mlsanity.com/paper/2402.14187/) (2024)
- [SINGA-Easy: An Easy-to-Use Framework for MultiModal Analysis](https://mlsanity.com/paper/2108.02572/) (2021)
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- [FeelsGoodMan: Inferring Semantics of Twitch Neologisms](https://mlsanity.com/paper/2108.08411/) (2021)
- [Text is NOT Enough: Integrating Visual Impressions into Open-domain Dialogue Generation](https://mlsanity.com/paper/2109.05778/) (2021)
- [Stock Price Prediction Under Anomalous Circumstances](https://mlsanity.com/paper/2109.15059/) (2021)
- [Discovering Personalized Semantics for Soft Attributes in Recommender Systems using Concept Activation Vectors](https://mlsanity.com/paper/2202.02830/) (2022)
- [Reinforced MOOCs Concept Recommendation in Heterogeneous Information Networks](https://mlsanity.com/paper/2203.11011/) (2022)
- [Explanation-Guided Fairness Testing through Genetic Algorithm](https://mlsanity.com/paper/2205.08335/) (2022)
- [Emoji-based Fine-grained Attention Network for Sentiment Analysis in the Microblog Comments](https://mlsanity.com/paper/2206.12262/) (2022)
- [Using Graph Neural Networks for Program Termination](https://mlsanity.com/paper/2207.14648/) (2022)
- [Tribe or Not? Critical Inspection of Group Differences Using TribalGram](https://mlsanity.com/paper/2303.09664/) (2023)
- [Unveiling Public Perceptions: Machine Learning-Based Sentiment Analysis of COVID-19 Vaccines in India](https://mlsanity.com/paper/2311.11435/) (2023)

## Abstract (arXiv)

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.