FINBERT: FINANCIAL SENTIMENT ANALYSIS WITH PRE-TRAINED LANGUAGE MODELS
While many sentiment classification solutions report high accuracy scores in product or movie review datasets, the performance of the methods in niche domains such as finance still largely falls behind. The reason of this gap is the domain-specific language, which decreases the applicability of existing models, and lack of quality labeled data to learn the new context of positive and negative in the specific domain. Transfer learning has been shown to be successful in adapting to new domains without large training data sets. In this paper, we explore the effectiveness of NLP transfer learning in financial sentiment classification. We introduce FinBERT, a language model based on BERT, which improved the state-of-the-art performance by 14 percentage points for a financial sentiment classification task in FinancialPhrasebank dataset.
This paper presents an analysis of the BERT language model on financial text. FinBERT is evaluated on two datasets from the financial domain: a sentiment prediction dataset (classification with 3 different classes) and a sentiment score prediction (the score is a float number between -1 and 1). I find the phrasing *FinBERT is a language model based on BERT* misleading; I think FinBERT is BERT trained on financial text. There is no modification that is done to the original BERT model. The paper presents several experiments using BERT as the language model and fine-tuning for the financial tasks. FinBERT is compared to a few baselines such as LSTMs with ElMO embeddings and ULMfit. I find interesting that the model performs better on the subset of the dataset for which there is perfect agreement between the annotators. I also find the results on training on financial data interesting. The results seem to indicate that further training on financial text does not seem to result in additional improvement when compared to original BERT. While I find the analysis and the experiments presented in the paper interesting, the novelty of the paper is rather low. There is no new idea introduced in this paper, it contains a series of experiments with BERT on financial text and tasks.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
FINBERT: FINANCIAL SENTIMENT ANALYSIS WITH PRE-TRAINED LANGUAGE MODELS
While many sentiment classification solutions report high accuracy scores in product or movie review datasets, the performance of the methods in niche domains such as finance still largely falls behind. The reason of this gap is the domain-specific language, which decreases the applicability of existing models, and lack of quality labeled data to learn the new context of positive and negative in the specific domain. Transfer learning has been shown to be successful in adapting to new domains without large training data sets. In this paper, we explore the effectiveness of NLP transfer learning in financial sentiment classification. We introduce FinBERT, a language model based on BERT, which improved the state-of-the-art performance by 14 percentage points for a financial sentiment classification task in FinancialPhrasebank dataset.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



This paper presents an analysis of the BERT language model on financial text. FinBERT is evaluated on two datasets from the financial domain: a sentiment prediction dataset (classification with 3 different classes) and a sentiment score prediction (the score is a float number between -1 and 1). I find the phrasing *FinBERT is a language model based on BERT* misleading; I think FinBERT is BERT trained on financial text. There is no modification that is done to the original BERT model. The paper presents several experiments using BERT as the language model and fine-tuning for the financial tasks. FinBERT is compared to a few baselines such as LSTMs with ElMO embeddings and ULMfit. I find interesting that the model performs better on the subset of the dataset for which there is perfect agreement between the annotators. I also find the results on training on financial data interesting. The results seem to indicate that further training on financial text does not seem to result in additional improvement when compared to original BERT. While I find the analysis and the experiments presented in the paper interesting, the novelty of the paper is rather low. There is no new idea introduced in this paper, it contains a series of experiments with BERT on financial text and tasks.
assistant
This paper presents an analysis of the BERT language model on financial text. FinBERT is evaluated on two datasets from the financial domain: a sentiment prediction dataset (classification with 3 different classes) and a sentiment score prediction (the score is a float number between -1 and 1). I find the phrasing *FinBERT is a language model based on BERT* misleading; I think FinBERT is BERT trained on financial text. There is no modification that is done to the original BERT model. The paper presents several experiments using BERT as the language model and fine-tuning for the financial tasks. FinBERT is compared to a few baselines such as LSTMs with ElMO embeddings and ULMfit. I find interesting that the model performs better on the subset of the dataset for which there is perfect agreement between the annotators. I also find the results on training on financial data interesting. The results seem to indicate that further training on financial text does not seem to result in additional improvement when compared to original BERT. While I find the analysis and the experiments presented in the paper interesting, the novelty of the paper is rather low. There is no new idea introduced in this paper, it contains a series of experiments with BERT on financial text and tasks.