我一直在尝试分析财务报表的情绪。在将财务词汇添加到词典后，我正在使用nltk.vader_lexicon模块。我正在使用此[[Loughran-McDonald词来增加here的财务词汇。
import csv
import pandas as pd
# stock market lexicon
stock_lex = pd.read_csv('C:/Users/ddutta070819/Downloads/EWS/StockSentimentTrading-master/lexicon_data/stock_lex.csv')
stock_lex['sentiment'] = (stock_lex['Aff_Score'] + stock_lex['Neg_Score'])/2
stock_lex = dict(zip(stock_lex.Item, stock_lex.sentiment))
stock_lex = {k:v for k,v in stock_lex.items() if len(k.split(' '))==1}
stock_lex_scaled = {}
for k, v in stock_lex.items():
if v > 0:
stock_lex_scaled[k] = v / max(stock_lex.values()) * 4
else:
stock_lex_scaled[k] = v / min(stock_lex.values()) * -4
# Loughran and McDonald
positive = []
with open('C:/Users/ddutta070819/Downloads/EWS/StockSentimentTrading-master/lexicon_data//lm_positive.csv', 'r') as f:
reader = csv.reader(f)
for row in reader:
positive.append(row[0].strip())
negative = []
with open('C:/Users/ddutta070819/Downloads/EWS/StockSentimentTrading-master/lexicon_data//lm_negative.csv', 'r') as f:
reader = csv.reader(f)
for row in reader:
entry = row[0].strip().split(" ")
if len(entry) > 1:
negative.extend(entry)
else:
negative.append(entry[0])
final_lex = {}
final_lex.update({word:2.0 for word in positive})
final_lex.update({word:-2.0 for word in negative})
final_lex.update(stock_lex_scaled)
final_lex.update(sia.lexicon)
sia.lexicon = final_lex
尽管总体结果有所改善，但是该模型似乎无法理解这些数字。对于前：
sia.polarity_scores('Royal Dutch Shell plc announced earnings results for the second quarter ended June 30, 2019. \ For the second quarter, the company announced total revenue was USD 91,838 million compared to USD 99,268 million a year \ ago. Net income was USD 2,998 million compared to USD 6,024 million a year ago. Basic earnings per share was USD 0.37 \ compared to USD 0.72 a year ago. For the half year, total revenue was USD 177,499 million compared to USD 190,382 million\ a year ago. Net income was USD 8,999 million compared to USD 11,923 million a year ago. Basic earnings per share was \ USD 1.11 compared to USD 1.44 a year ago. Diluted earnings per share was USD 1.1 compared to USD 1.42 a year ago.')-0.81这是绝对正确的，但是即使我更改了数字：
sia.polarity_scores('Royal Dutch Shell plc announced earnings results for the second quarter ended June 30, 2019. \ For the second quarter, the company announced total revenue was USD 91,838 million compared to USD 69,268 million a year \ ago. Net income was USD 2,998 million compared to USD 1,024 million a year ago. Basic earnings per share was USD 0.37 \ compared to USD 0.17 a year ago. For the half year, total revenue was USD 177,499 million compared to USD 150,382 million\ a year ago. Net income was USD 8,999 million compared to USD 6,923 million a year ago. Basic earnings per share was \ USD 1.11 compared to USD 1.04 a year ago. Diluted earnings per share was USD 1.1 compared to USD 1.02 a year ago.')-0.81提供的情感分数仍然为负。
有没有一种方法可以帮助模型根据所写文本的上下文来理解这些数字？