Improving Distributed Representations of Tweets - Present and Future

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.Comment: To be presented in Student Research Workshop (SRW) at ACL 201

Improving Distributed Representations of Tweets - Present and Future

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.Comment: To be presented in Student Research Workshop (SRW) at ACL 201

The Effect of Concept Sentence Learning Model on Students' Learning Outcomes

This study aims to determine the influence of using the Concept sentence learning model on the learning outcomes of Tamansiswa Pematangsiantar Middle School students. The method used in this study is an experimental method with a randomized control group pretest-posttest design. The research was carried out in three stages, namely the preparation stage, the implementation stage, and the final stage. Data analysis was carried out quantitatively, namely descriptive and comparative. The data analysis technique used is descriptive analysis and inferential statistical analysis. Data testing was carried out in two stages, namely the analysis requirements test (normality test and homogeneity test), and hypothesis testing was carried out by statistical t-test. Research data were analyzed using SPSS version 21.0. From the results of the study, the average value of the post-test for the experimental class was 88.68, while the average post-test for the control class was 85.13. From the results of testing the hypothesis obtained tcount (2.338) > ttable (1.992), then Ho is rejected and Ha is accepted meaning that there is a significant influence of applying the Concept Sentence learning model to student learning outcomes at Tamansiswa Pematangsiantar Middle School. It was concluded that there was an effect of applying the Concept Sentence learning model to the learning outcomes student of senior high school Tamansiswa Pematangsiantar