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  ### Dataset Description
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- The Twitter Financial News dataset is an English-language dataset containing an annotated corpus of finance-related tweets. The dataset is split into two groups:
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- 1. Topic classification: 21,107 documents annotated with 20 labels:
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-
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- ```python
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- topics = {
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- "LABEL_0": "Analyst Update",
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- "LABEL_1": "Fed | Central Banks",
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- "LABEL_2": "Company | Product News",
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- "LABEL_3": "Treasuries | Corporate Debt",
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- "LABEL_4": "Dividend",
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- "LABEL_5": "Earnings",
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- "LABEL_6": "Energy | Oil",
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- "LABEL_7": "Financials",
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- "LABEL_8": "Currencies",
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- "LABEL_9": "General News | Opinion",
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- "LABEL_10": "Gold | Metals | Materials",
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- "LABEL_11": "IPO",
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- "LABEL_12": "Legal | Regulation",
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- "LABEL_13": "M&A | Investments",
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- "LABEL_14": "Macro",
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- "LABEL_15": "Markets",
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- "LABEL_16": "Politics",
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- "LABEL_17": "Personnel Change",
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- "LABEL_18": "Stock Commentary",
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- "LABEL_19": "Stock Movement",
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- }
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- ```
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-
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- 2. Sentiment analysis: 11,932 documents annotated with 3 labels:
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  ```python
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  sentiments = {
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  The data was collected using the Twitter API. The current dataset supports the multi-class classification task.
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- ### Task 1: Sentiment Analysis
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  # Data Splits
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  There are 2 splits: train and validation. Below are the statistics:
 
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  ### Dataset Description
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+ The Twitter Financial News dataset is an English-language dataset containing an annotated corpus of finance-related tweets. This dataset is used to classify finance-related tweets for their sentiment.
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+ 1. The dataset holds 11,932 documents annotated with 3 labels:
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ```python
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  sentiments = {
 
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  The data was collected using the Twitter API. The current dataset supports the multi-class classification task.
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+ ### Task: Sentiment Analysis
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  # Data Splits
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  There are 2 splits: train and validation. Below are the statistics: