Improving Distributed Representations of Tweets - Present and Future
This work provides a foundational survey for researchers and practitioners working on tweet-based applications like sentiment analysis, though it is incremental as it synthesizes existing knowledge without new empirical results.
The authors tackled the lack of systematic surveys on unsupervised representation learning models for tweets, organizing existing literature by objective function and proposing future directions to advance the field.
Unsupervised representation learning for tweets is an important research field which helps in solving several business applications such as sentiment analysis, hashtag prediction, paraphrase detection and microblog ranking. A good tweet representation learning model must handle the idiosyncratic nature of tweets which poses several challenges such as short length, informal words, unusual grammar and misspellings. However, there is a lack of prior work which surveys the representation learning models with a focus on tweets. In this work, we organize the models based on its objective function which aids the understanding of the literature. We also provide interesting future directions, which we believe are fruitful in advancing this field by building high-quality tweet representation learning models.