Sentiment Embeddings with Applications to Sentiment Analysis

By Duyu Tang, Furu Wei, Bing Qin, Nan Yang, Ting-Wei Liu, M. Zhou. IEEE Transactions on Knowledge and Data Engineering, 2016

319 citations recorded by the source corpus.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/e5ca66fe2ae33687a5d1012cf9b1a77b2fc0a0f2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Shuai Wang
2. B. Liu
3. Lei Zhang
4. Bing Liu
5. K. Robert Lai
6. Liang-Chih Yu
7. Xuejie Zhang
8. Jin Wang

Citations in the corpus

• Interpretation of Semantic Tweet Representations (2017, methodology): “Researchers in Twitter analytics have found these representation learning models to be very effective for several critical tasks such as sentiment analysis [4], [14], semantic textual similarity computation [8], hashtag identification [9], etc.”
• Sentiment Analysis of Comment Texts Based on BiLSTM (2019, background): “[31] introduced several neural networks to effectively encode context and sentiment information into word embeddings.”
• Refining Word Embeddings for Sentiment Analysis (2017, methodology): “To enhance the performance of distinguishing words with similar vector representations but opposite sentiment polarities, recent studies have suggested learning sentiment embeddings from labeled data in a supervised manner (Maas et al., 2011; Labutov and Lipson, 2013; Lan et al., 2016; Ren et al., 2”
• Deep learning for sentiment analysis: A survey (2018, background): “Tang et al. 40 applied user representations and product representations in review classification.”
• Debiasing Gender biased Hindi Words with Word-embedding (2019, background): “Applications of pre-trained word-embeddings include various downstream natural language processing (NLP) tasks such as machine translation [1, 2, 3], sentiment analysis [4], dialogue generation [5], contextualized word representations [6, 7].”
• A Comparative Study on Affective Cues in Text Embeddings Across Psychological Emotion Theories (2026, unknown): “Word vectors were retrained from scratch incorporating supervised affective contexts into the objective function [49].”