Venue: ICLR 2023 TinyPapers

Reviews:

final_score: rating: High Potential (HP): a submission which meets the reviewing criteria and has potential to make an impact on the field
review_content: summary_of_contributions: The paper studies computationally efficient methods for hope speech detection in online content, specifically YouTube comments (limited by the choice of the dataset). They clearly demonstrate that traditional machine learning models, combined with appropriate input representations, can perform comparably or even better than complex deep learning models.
strengths_and_weaknesses: Strengths:

1. The paper tackles an important problem in an understudied task, i.e, computationally efficient approaches in hope speech detection without compromising on the quality of the results. 

2. The experiments are well designed to provide clear evidence that the studied traditional machine learning models with appropriate input embeddings are comparable to the common deep learning based approaches in the literature. 

3. The insights from the experiments could be of significant value in practice, especially due to the fact that simpler models are often easy to deploy at scale of online content and social media networks. 

4. Although not available at the time of review, the authors promise to release the code (hopefully by the conference dates) which, along with the detailed experiment specifications in the appendix, would make it easy to reproduce the results. 

5. The paper is well-written overall and fits perfectly into the Tiny Paper track! Great job!

Weaknesses:

1. The paper could benefit from a more detailed explanation of the rationale behind the choice of embeddings, the used traditional ML models, and the data augmentation techniques. 
suggested_changes: Minor suggestions:

1. In abstract: I understand the intention of the line "Hope speech refers to YouTube comments/posts ..". However, I think its okay to give general definition of hope speech here and then later make it clear that the experiments are limited to hope speech in YouTube comments/posts since that's what the available datasets contain. 

2. There is an unnecessary period after quotation marks in the abstract after insight." and codes should be code.
review_title: null

final_score: rating: Great Start (GS): a submission which meets some of the reviewing criteria but has room for improvement
review_content: summary_of_contributions: The authors tackle the problem of detecting "hopeful" content. They  apply various methods to existing datasets. Some conclusions are drawn.
strengths_and_weaknesses: - Strengths: The problem is relevant, the results potentially interesting

- Weaknesses: The paper is poorly written and exremely obscure.

suggested_changes: - I do not understand which of the various reported results the authors claim to be their own, as opposed to existing results.

- What is the "Shared Task"? 

- What  does "Shared Task Rank 1" mean?

- Why only train BERT-like models  for the second task? Why  not apply  the same models as in the first task? And vice-versa?

- Despite the "tl/dr", I couldn't find any analysis of submissions to the previous Hope Speech Shared tasks. Where is it?
review_title: null

Meta-review: metareview: The two reviewers differ in their evaluations of the paper. I tend to agree more with reviewer `eHcs` after reading the paper. I found the paper lacking in clear descriptions of what was done and what their results were.

Two tables in the main text seem to summarize the results but it is unclear what these tables are displaying. Assuming that "Shared Task Rank 1" indicates the author's results, it seems that the model is performing much more poorly than the other methods (Table 2). According to the authors, one should take Table 2 results more seriously because this table summarizes the Macro F1 score which is "more meaningful" because "the class imbalance would not lead to elevated Weighted F1 scores."

confidence: 3: The area chair is somewhat confident
Decision: Invite to archive