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Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN. | STOCK: 27/09/2018 LAST_PRICE: -0.0002249111600918 PX_VOLUME: 13.738 VOLATILITY_10D: 14.954 VOLATILITY_30D: 1.0 LSTM_POLARITY: 0.0 TEXTBLOB_POLARITY: @Mastercard | Predicted 1_DAY_RETURN: -0.0008546624083486
Predicted 2_DAY_RETURN: -0.0043182942737619
Predicted 7_DAY_RETURN: 2645186.0 |
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned. | TWEET: "@Google Happy 20th Google. I depend on you 99.99% 😆" STOCK: nan DATE: nan | Sentiment: (Provide sentiment here)
Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is nan and the TextBlob polarity score is nan. |
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends. | STOCK: nan 1_DAY_RETURN: nan 2_DAY_RETURN: nan 3_DAY_RETURN: nan 7_DAY_RETURN: nan | The stock shows a neutral return trend over the specified periods. |
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN. | STOCK: nan LAST_PRICE: nan PX_VOLUME: nan VOLATILITY_10D: nan VOLATILITY_30D: nan LSTM_POLARITY: nan TEXTBLOB_POLARITY: nan | Predicted 1_DAY_RETURN: nan
Predicted 2_DAY_RETURN: nan
Predicted 7_DAY_RETURN: nan |
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned. | TWEET: "Google" STOCK: 27/09/2018 DATE: 1207.36 | Sentiment: (Provide sentiment here)
Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is 0.4 and the TextBlob polarity score is @Google. |
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends. | STOCK: 27/09/2018 1_DAY_RETURN: -0.0111565730188177 2_DAY_RETURN: -0.0230254439438112 3_DAY_RETURN: -0.013078120858733 7_DAY_RETURN: 1813652.0 | The stock shows a consistent positive return trend over the specified periods. |
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN. | STOCK: 27/09/2018 LAST_PRICE: -0.0110157699443413 PX_VOLUME: 18.416 VOLATILITY_10D: 17.695 VOLATILITY_30D: 1.0 LSTM_POLARITY: 0.4 TEXTBLOB_POLARITY: @Google | Predicted 1_DAY_RETURN: -0.0111565730188177
Predicted 2_DAY_RETURN: -0.0230254439438112
Predicted 7_DAY_RETURN: 1813652.0 |
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned. | TWEET: "RT @alexanderbruz: Christine Blasey Ford just admitted she added a second front door so she could rent out rooms in her house to @Google in…" STOCK: nan DATE: nan | Sentiment: (Provide sentiment here)
Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is nan and the TextBlob polarity score is nan. |
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends. | STOCK: nan 1_DAY_RETURN: nan 2_DAY_RETURN: nan 3_DAY_RETURN: nan 7_DAY_RETURN: nan | The stock shows a neutral return trend over the specified periods. |
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN. | STOCK: nan LAST_PRICE: nan PX_VOLUME: nan VOLATILITY_10D: nan VOLATILITY_30D: nan LSTM_POLARITY: nan TEXTBLOB_POLARITY: nan | Predicted 1_DAY_RETURN: nan
Predicted 2_DAY_RETURN: nan
Predicted 7_DAY_RETURN: nan |
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned. | TWEET: "Ford" STOCK: 27/09/2018 DATE: 9.23 | Sentiment: (Provide sentiment here)
Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is 0.0 and the TextBlob polarity score is @Google. |
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends. | STOCK: 27/09/2018 1_DAY_RETURN: 0.0173347778981581 2_DAY_RETURN: 0.0390032502708558 3_DAY_RETURN: 0.0628385698808234 7_DAY_RETURN: 57272192.0 | The stock shows a consistent positive return trend over the specified periods. |
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN. | STOCK: 27/09/2018 LAST_PRICE: 0.0043336944745394 PX_VOLUME: 24.558000000000003 VOLATILITY_10D: 23.023000000000003 VOLATILITY_30D: 1.0 LSTM_POLARITY: 0.0 TEXTBLOB_POLARITY: @Google | Predicted 1_DAY_RETURN: 0.0173347778981581
Predicted 2_DAY_RETURN: 0.0390032502708558
Predicted 7_DAY_RETURN: 57272192.0 |
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned. | TWEET: "RT @Reuters: Christine Blasey Ford gives testimony of alleged sexual assault by Supreme Court nominee Kavanaugh. Watch live: https://t.co/I…" STOCK: nan DATE: nan | Sentiment: (Provide sentiment here)
Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is nan and the TextBlob polarity score is nan. |
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends. | STOCK: nan 1_DAY_RETURN: nan 2_DAY_RETURN: nan 3_DAY_RETURN: nan 7_DAY_RETURN: nan | The stock shows a neutral return trend over the specified periods. |
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN. | STOCK: nan LAST_PRICE: nan PX_VOLUME: nan VOLATILITY_10D: nan VOLATILITY_30D: nan LSTM_POLARITY: nan TEXTBLOB_POLARITY: nan | Predicted 1_DAY_RETURN: nan
Predicted 2_DAY_RETURN: nan
Predicted 7_DAY_RETURN: nan |
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned. | TWEET: "Ford" STOCK: 27/09/2018 DATE: 9.23 | Sentiment: (Provide sentiment here)
Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is 0.2 and the TextBlob polarity score is @Reuters. |
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends. | STOCK: 27/09/2018 1_DAY_RETURN: 0.0173347778981581 2_DAY_RETURN: 0.0390032502708558 3_DAY_RETURN: 0.0628385698808234 7_DAY_RETURN: 57272192.0 | The stock shows a consistent positive return trend over the specified periods. |
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN. | STOCK: 27/09/2018 LAST_PRICE: 0.0043336944745394 PX_VOLUME: 24.558000000000003 VOLATILITY_10D: 23.023000000000003 VOLATILITY_30D: 1.0 LSTM_POLARITY: 0.2 TEXTBLOB_POLARITY: @Reuters | Predicted 1_DAY_RETURN: 0.0173347778981581
Predicted 2_DAY_RETURN: 0.0390032502708558
Predicted 7_DAY_RETURN: 57272192.0 |
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned. | TWEET: "RT @alexanderbruz: Christine Blasey Ford just admitted she added a second front door so she could rent out rooms in her house to @Google in…" STOCK: nan DATE: nan | Sentiment: (Provide sentiment here)
Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is nan and the TextBlob polarity score is nan. |
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends. | STOCK: nan 1_DAY_RETURN: nan 2_DAY_RETURN: nan 3_DAY_RETURN: nan 7_DAY_RETURN: nan | The stock shows a neutral return trend over the specified periods. |
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN. | STOCK: nan LAST_PRICE: nan PX_VOLUME: nan VOLATILITY_10D: nan VOLATILITY_30D: nan LSTM_POLARITY: nan TEXTBLOB_POLARITY: nan | Predicted 1_DAY_RETURN: nan
Predicted 2_DAY_RETURN: nan
Predicted 7_DAY_RETURN: nan |
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned. | TWEET: "Ford" STOCK: 27/09/2018 DATE: 9.23 | Sentiment: (Provide sentiment here)
Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is 0.0 and the TextBlob polarity score is @Google. |
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends. | STOCK: 27/09/2018 1_DAY_RETURN: 0.0173347778981581 2_DAY_RETURN: 0.0390032502708558 3_DAY_RETURN: 0.0628385698808234 7_DAY_RETURN: 57272192.0 | The stock shows a consistent positive return trend over the specified periods. |
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN. | STOCK: 27/09/2018 LAST_PRICE: 0.0043336944745394 PX_VOLUME: 24.558000000000003 VOLATILITY_10D: 23.023000000000003 VOLATILITY_30D: 1.0 LSTM_POLARITY: 0.0 TEXTBLOB_POLARITY: @Google | Predicted 1_DAY_RETURN: 0.0173347778981581
Predicted 2_DAY_RETURN: 0.0390032502708558
Predicted 7_DAY_RETURN: 57272192.0 |
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned. | TWEET: "RT @alexanderbruz: Christine Blasey Ford just admitted she added a second front door so she could rent out rooms in her house to @Google in…" STOCK: nan DATE: nan | Sentiment: (Provide sentiment here)
Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is nan and the TextBlob polarity score is nan. |
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends. | STOCK: nan 1_DAY_RETURN: nan 2_DAY_RETURN: nan 3_DAY_RETURN: nan 7_DAY_RETURN: nan | The stock shows a neutral return trend over the specified periods. |
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN. | STOCK: nan LAST_PRICE: nan PX_VOLUME: nan VOLATILITY_10D: nan VOLATILITY_30D: nan LSTM_POLARITY: nan TEXTBLOB_POLARITY: nan | Predicted 1_DAY_RETURN: nan
Predicted 2_DAY_RETURN: nan
Predicted 7_DAY_RETURN: nan |
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned. | TWEET: "Ford" STOCK: 27/09/2018 DATE: 9.23 | Sentiment: (Provide sentiment here)
Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is 0.0 and the TextBlob polarity score is @Google. |
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends. | STOCK: 27/09/2018 1_DAY_RETURN: 0.0173347778981581 2_DAY_RETURN: 0.0390032502708558 3_DAY_RETURN: 0.0628385698808234 7_DAY_RETURN: 57272192.0 | The stock shows a consistent positive return trend over the specified periods. |
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN. | STOCK: 27/09/2018 LAST_PRICE: 0.0043336944745394 PX_VOLUME: 24.558000000000003 VOLATILITY_10D: 23.023000000000003 VOLATILITY_30D: 1.0 LSTM_POLARITY: 0.0 TEXTBLOB_POLARITY: @Google | Predicted 1_DAY_RETURN: 0.0173347778981581
Predicted 2_DAY_RETURN: 0.0390032502708558
Predicted 7_DAY_RETURN: 57272192.0 |
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned. | TWEET: "RT @Reuters: MORE: Christine Blasey Ford says she is 100 percent certain Kavanaugh assaulted her https://t.co/Llkc1C6lG5" STOCK: nan DATE: nan | Sentiment: (Provide sentiment here)
Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is nan and the TextBlob polarity score is nan. |
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends. | STOCK: nan 1_DAY_RETURN: nan 2_DAY_RETURN: nan 3_DAY_RETURN: nan 7_DAY_RETURN: nan | The stock shows a neutral return trend over the specified periods. |
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN. | STOCK: nan LAST_PRICE: nan PX_VOLUME: nan VOLATILITY_10D: nan VOLATILITY_30D: nan LSTM_POLARITY: nan TEXTBLOB_POLARITY: nan | Predicted 1_DAY_RETURN: nan
Predicted 2_DAY_RETURN: nan
Predicted 7_DAY_RETURN: nan |
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned. | TWEET: "Ford" STOCK: 27/09/2018 DATE: 9.23 | Sentiment: (Provide sentiment here)
Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is 0.35714285714285715 and the TextBlob polarity score is @Reuters. |
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends. | STOCK: 27/09/2018 1_DAY_RETURN: 0.0173347778981581 2_DAY_RETURN: 0.0390032502708558 3_DAY_RETURN: 0.0628385698808234 7_DAY_RETURN: 57272192.0 | The stock shows a consistent positive return trend over the specified periods. |
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN. | STOCK: 27/09/2018 LAST_PRICE: 0.0043336944745394 PX_VOLUME: 24.558000000000003 VOLATILITY_10D: 23.023000000000003 VOLATILITY_30D: -1.0 LSTM_POLARITY: 0.35714285714285715 TEXTBLOB_POLARITY: @Reuters | Predicted 1_DAY_RETURN: 0.0173347778981581
Predicted 2_DAY_RETURN: 0.0390032502708558
Predicted 7_DAY_RETURN: 57272192.0 |
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned. | TWEET: "RT @alexanderbruz: Christine Blasey Ford just admitted she added a second front door so she could rent out rooms in her house to @Google in…" STOCK: nan DATE: nan | Sentiment: (Provide sentiment here)
Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is nan and the TextBlob polarity score is nan. |
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends. | STOCK: nan 1_DAY_RETURN: nan 2_DAY_RETURN: nan 3_DAY_RETURN: nan 7_DAY_RETURN: nan | The stock shows a neutral return trend over the specified periods. |
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN. | STOCK: nan LAST_PRICE: nan PX_VOLUME: nan VOLATILITY_10D: nan VOLATILITY_30D: nan LSTM_POLARITY: nan TEXTBLOB_POLARITY: nan | Predicted 1_DAY_RETURN: nan
Predicted 2_DAY_RETURN: nan
Predicted 7_DAY_RETURN: nan |
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned. | TWEET: "Ford" STOCK: 27/09/2018 DATE: 9.23 | Sentiment: (Provide sentiment here)
Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is 0.0 and the TextBlob polarity score is @Google. |
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends. | STOCK: 27/09/2018 1_DAY_RETURN: 0.0173347778981581 2_DAY_RETURN: 0.0390032502708558 3_DAY_RETURN: 0.0628385698808234 7_DAY_RETURN: 57272192.0 | The stock shows a consistent positive return trend over the specified periods. |
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN. | STOCK: 27/09/2018 LAST_PRICE: 0.0043336944745394 PX_VOLUME: 24.558000000000003 VOLATILITY_10D: 23.023000000000003 VOLATILITY_30D: 1.0 LSTM_POLARITY: 0.0 TEXTBLOB_POLARITY: @Google | Predicted 1_DAY_RETURN: 0.0173347778981581
Predicted 2_DAY_RETURN: 0.0390032502708558
Predicted 7_DAY_RETURN: 57272192.0 |
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned. | TWEET: "@Martin23kings @iAm_erica @booredatwork @Apple Still no response about me calling Apple out because it’s a better n… https://t.co/w0Goqicdjg" STOCK: nan DATE: nan | Sentiment: (Provide sentiment here)
Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is nan and the TextBlob polarity score is nan. |
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends. | STOCK: nan 1_DAY_RETURN: nan 2_DAY_RETURN: nan 3_DAY_RETURN: nan 7_DAY_RETURN: nan | The stock shows a neutral return trend over the specified periods. |
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN. | STOCK: nan LAST_PRICE: nan PX_VOLUME: nan VOLATILITY_10D: nan VOLATILITY_30D: nan LSTM_POLARITY: nan TEXTBLOB_POLARITY: nan | Predicted 1_DAY_RETURN: nan
Predicted 2_DAY_RETURN: nan
Predicted 7_DAY_RETURN: nan |
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned. | TWEET: "Apple" STOCK: 27/09/2018 DATE: 224.95 | Sentiment: (Provide sentiment here)
Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is 0.5 and the TextBlob polarity score is @Apple. |
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends. | STOCK: 27/09/2018 1_DAY_RETURN: -0.0122693931984885 2_DAY_RETURN: -0.0184929984440986 3_DAY_RETURN: -0.0218715270060012 7_DAY_RETURN: 30181227.0 | The stock shows a consistent positive return trend over the specified periods. |
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN. | STOCK: 27/09/2018 LAST_PRICE: -0.020137808401867 PX_VOLUME: 22.98 VOLATILITY_10D: 20.811 VOLATILITY_30D: -1.0 LSTM_POLARITY: 0.5 TEXTBLOB_POLARITY: @Apple | Predicted 1_DAY_RETURN: -0.0122693931984885
Predicted 2_DAY_RETURN: -0.0184929984440986
Predicted 7_DAY_RETURN: 30181227.0 |
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned. | TWEET: "RT @alexanderbruz: Christine Blasey Ford just admitted she added a second front door so she could rent out rooms in her house to @Google in…" STOCK: nan DATE: nan | Sentiment: (Provide sentiment here)
Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is nan and the TextBlob polarity score is nan. |
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends. | STOCK: nan 1_DAY_RETURN: nan 2_DAY_RETURN: nan 3_DAY_RETURN: nan 7_DAY_RETURN: nan | The stock shows a neutral return trend over the specified periods. |
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN. | STOCK: nan LAST_PRICE: nan PX_VOLUME: nan VOLATILITY_10D: nan VOLATILITY_30D: nan LSTM_POLARITY: nan TEXTBLOB_POLARITY: nan | Predicted 1_DAY_RETURN: nan
Predicted 2_DAY_RETURN: nan
Predicted 7_DAY_RETURN: nan |
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned. | TWEET: "Ford" STOCK: 27/09/2018 DATE: 9.23 | Sentiment: (Provide sentiment here)
Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is 0.0 and the TextBlob polarity score is @Google. |
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends. | STOCK: 27/09/2018 1_DAY_RETURN: 0.0173347778981581 2_DAY_RETURN: 0.0390032502708558 3_DAY_RETURN: 0.0628385698808234 7_DAY_RETURN: 57272192.0 | The stock shows a consistent positive return trend over the specified periods. |
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN. | STOCK: 27/09/2018 LAST_PRICE: 0.0043336944745394 PX_VOLUME: 24.558000000000003 VOLATILITY_10D: 23.023000000000003 VOLATILITY_30D: 1.0 LSTM_POLARITY: 0.0 TEXTBLOB_POLARITY: @Google | Predicted 1_DAY_RETURN: 0.0173347778981581
Predicted 2_DAY_RETURN: 0.0390032502708558
Predicted 7_DAY_RETURN: 57272192.0 |
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned. | TWEET: "RT @Reuters: When asked if her allegations against Brett Kavanaugh could be a case of mistaken identity, Ford said 'absolutely not.' Watch…
" STOCK: Ford DATE: 27/09/2018 | Sentiment: (Provide sentiment here)
Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is -1 and the TextBlob polarity score is -0.1. |
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends. | STOCK: Ford 1_DAY_RETURN: 0.0043336944745394 2_DAY_RETURN: 0.0173347778981581 3_DAY_RETURN: 0.0390032502708558 7_DAY_RETURN: 0.0628385698808234 | The stock shows a consistent positive return trend over the specified periods. |
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN. | STOCK: Ford LAST_PRICE: 9.23 PX_VOLUME: 57272192.0 VOLATILITY_10D: 24.558000000000003 VOLATILITY_30D: 23.023000000000003 LSTM_POLARITY: -1 TEXTBLOB_POLARITY: -0.1 | Predicted 1_DAY_RETURN: 0.0043336944745394
Predicted 2_DAY_RETURN: 0.0173347778981581
Predicted 7_DAY_RETURN: 0.0628385698808234 |
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned. | TWEET: "RT @Reuters: 'I was pushed onto the bed and Brett got on top of me,' says Christine Blasey Ford in her testimony against Kavanaugh. Watch l…
" STOCK: Ford DATE: 27/09/2018 | Sentiment: (Provide sentiment here)
Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is -1 and the TextBlob polarity score is 0.5. |
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends. | STOCK: Ford 1_DAY_RETURN: 0.0043336944745394 2_DAY_RETURN: 0.0173347778981581 3_DAY_RETURN: 0.0390032502708558 7_DAY_RETURN: 0.0628385698808234 | The stock shows a consistent positive return trend over the specified periods. |
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN. | STOCK: Ford LAST_PRICE: 9.23 PX_VOLUME: 57272192.0 VOLATILITY_10D: 24.558000000000003 VOLATILITY_30D: 23.023000000000003 LSTM_POLARITY: -1 TEXTBLOB_POLARITY: 0.5 | Predicted 1_DAY_RETURN: 0.0043336944745394
Predicted 2_DAY_RETURN: 0.0173347778981581
Predicted 7_DAY_RETURN: 0.0628385698808234 |
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned. | TWEET: "RT @Reuters: ‘They were laughing.’ Christine Blasey Ford says her strongest memory of the alleged incident was 'uproarious laughter' betwee…" STOCK: nan DATE: nan | Sentiment: (Provide sentiment here)
Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is nan and the TextBlob polarity score is nan. |
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends. | STOCK: nan 1_DAY_RETURN: nan 2_DAY_RETURN: nan 3_DAY_RETURN: nan 7_DAY_RETURN: nan | The stock shows a neutral return trend over the specified periods. |
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN. | STOCK: nan LAST_PRICE: nan PX_VOLUME: nan VOLATILITY_10D: nan VOLATILITY_30D: nan LSTM_POLARITY: nan TEXTBLOB_POLARITY: nan | Predicted 1_DAY_RETURN: nan
Predicted 2_DAY_RETURN: nan
Predicted 7_DAY_RETURN: nan |
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned. | TWEET: "Ford" STOCK: 27/09/2018 DATE: 9.23 | Sentiment: (Provide sentiment here)
Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is -0.1 and the TextBlob polarity score is @Reuters. |
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends. | STOCK: 27/09/2018 1_DAY_RETURN: 0.0173347778981581 2_DAY_RETURN: 0.0390032502708558 3_DAY_RETURN: 0.0628385698808234 7_DAY_RETURN: 57272192.0 | The stock shows a consistent positive return trend over the specified periods. |
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN. | STOCK: 27/09/2018 LAST_PRICE: 0.0043336944745394 PX_VOLUME: 24.558000000000003 VOLATILITY_10D: 23.023000000000003 VOLATILITY_30D: -1.0 LSTM_POLARITY: -0.1 TEXTBLOB_POLARITY: @Reuters | Predicted 1_DAY_RETURN: 0.0173347778981581
Predicted 2_DAY_RETURN: 0.0390032502708558
Predicted 7_DAY_RETURN: 57272192.0 |
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned. | TWEET: "RT @TexasChE: Wow!! Thanks to @exxonmobil and their generous employees for their strong and continued support of @UTAustin. Exxon employs m…" STOCK: nan DATE: nan | Sentiment: (Provide sentiment here)
Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is nan and the TextBlob polarity score is nan. |
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends. | STOCK: nan 1_DAY_RETURN: nan 2_DAY_RETURN: nan 3_DAY_RETURN: nan 7_DAY_RETURN: nan | The stock shows a neutral return trend over the specified periods. |
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN. | STOCK: nan LAST_PRICE: nan PX_VOLUME: nan VOLATILITY_10D: nan VOLATILITY_30D: nan LSTM_POLARITY: nan TEXTBLOB_POLARITY: nan | Predicted 1_DAY_RETURN: nan
Predicted 2_DAY_RETURN: nan
Predicted 7_DAY_RETURN: nan |
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned. | TWEET: "Exxon" STOCK: 27/09/2018 DATE: 85.77 | Sentiment: (Provide sentiment here)
Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is 0.15625 and the TextBlob polarity score is @exxonmobil. |
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends. | STOCK: 27/09/2018 1_DAY_RETURN: 0.0085111344292876 2_DAY_RETURN: 0.0096770432552174 3_DAY_RETURN: -0.0110761338463332 7_DAY_RETURN: 7895430.0 | The stock shows a consistent positive return trend over the specified periods. |
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN. | STOCK: 27/09/2018 LAST_PRICE: 0.000116590882593 PX_VOLUME: 11.773 VOLATILITY_10D: 12.938 VOLATILITY_30D: 1.0 LSTM_POLARITY: 0.15625 TEXTBLOB_POLARITY: @exxonmobil | Predicted 1_DAY_RETURN: 0.0085111344292876
Predicted 2_DAY_RETURN: 0.0096770432552174
Predicted 7_DAY_RETURN: 7895430.0 |
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned. | TWEET: "RT @Reuters: Sales of Nike gear have soared 61 percent in the 10-day period after the company's controversial decision to feature former NF…" STOCK: nan DATE: nan | Sentiment: (Provide sentiment here)
Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is nan and the TextBlob polarity score is nan. |
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends. | STOCK: nan 1_DAY_RETURN: nan 2_DAY_RETURN: nan 3_DAY_RETURN: nan 7_DAY_RETURN: nan | The stock shows a neutral return trend over the specified periods. |
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN. | STOCK: nan LAST_PRICE: nan PX_VOLUME: nan VOLATILITY_10D: nan VOLATILITY_30D: nan LSTM_POLARITY: nan TEXTBLOB_POLARITY: nan | Predicted 1_DAY_RETURN: nan
Predicted 2_DAY_RETURN: nan
Predicted 7_DAY_RETURN: nan |
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned. | TWEET: "Nike" STOCK: 27/09/2018 DATE: 84.54 | Sentiment: (Provide sentiment here)
Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is 0.275 and the TextBlob polarity score is @Reuters. |
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends. | STOCK: 27/09/2018 1_DAY_RETURN: 0.0029571800331204 2_DAY_RETURN: -0.0031937544357701 3_DAY_RETURN: 0.0098178377099597 7_DAY_RETURN: 6080564.0 | The stock shows a consistent positive return trend over the specified periods. |
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN. | STOCK: 27/09/2018 LAST_PRICE: -0.0099361249112846 PX_VOLUME: 20.623 VOLATILITY_10D: 20.092 VOLATILITY_30D: 1.0 LSTM_POLARITY: 0.275 TEXTBLOB_POLARITY: @Reuters | Predicted 1_DAY_RETURN: 0.0029571800331204
Predicted 2_DAY_RETURN: -0.0031937544357701
Predicted 7_DAY_RETURN: 6080564.0 |
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned. | TWEET: "Ldea Galaxy S9 Plus Case, Silicone Shockproof Tempered Glass Back Cover Shell... https://t.co/YMXbb96JJi via @amazon
" STOCK: Shell DATE: 27/09/2018 | Sentiment: (Provide sentiment here)
Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is 1 and the TextBlob polarity score is 0.0. |
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends. | STOCK: Shell 1_DAY_RETURN: 0.0013310515307092 2_DAY_RETURN: 0.009127210496292 3_DAY_RETURN: -0.0133105153070926 7_DAY_RETURN: -0.0439247005134055 | The stock shows a consistent negative return trend over the specified periods. |
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN. | STOCK: Shell LAST_PRICE: 2629.5 PX_VOLUME: 3346031.0 VOLATILITY_10D: 17.052 VOLATILITY_30D: 17.285 LSTM_POLARITY: 1 TEXTBLOB_POLARITY: 0.0 | Predicted 1_DAY_RETURN: 0.0013310515307092
Predicted 2_DAY_RETURN: 0.009127210496292
Predicted 7_DAY_RETURN: -0.0439247005134055 |
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned. | TWEET: "RT @alexanderbruz: Christine Blasey Ford just admitted she added a second front door so she could rent out rooms in her house to @Google in…" STOCK: nan DATE: nan | Sentiment: (Provide sentiment here)
Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is nan and the TextBlob polarity score is nan. |
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends. | STOCK: nan 1_DAY_RETURN: nan 2_DAY_RETURN: nan 3_DAY_RETURN: nan 7_DAY_RETURN: nan | The stock shows a neutral return trend over the specified periods. |
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN. | STOCK: nan LAST_PRICE: nan PX_VOLUME: nan VOLATILITY_10D: nan VOLATILITY_30D: nan LSTM_POLARITY: nan TEXTBLOB_POLARITY: nan | Predicted 1_DAY_RETURN: nan
Predicted 2_DAY_RETURN: nan
Predicted 7_DAY_RETURN: nan |
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned. | TWEET: "Ford" STOCK: 27/09/2018 DATE: 9.23 | Sentiment: (Provide sentiment here)
Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is 0.0 and the TextBlob polarity score is @Google. |
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends. | STOCK: 27/09/2018 1_DAY_RETURN: 0.0173347778981581 2_DAY_RETURN: 0.0390032502708558 3_DAY_RETURN: 0.0628385698808234 7_DAY_RETURN: 57272192.0 | The stock shows a consistent positive return trend over the specified periods. |
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN. | STOCK: 27/09/2018 LAST_PRICE: 0.0043336944745394 PX_VOLUME: 24.558000000000003 VOLATILITY_10D: 23.023000000000003 VOLATILITY_30D: 1.0 LSTM_POLARITY: 0.0 TEXTBLOB_POLARITY: @Google | Predicted 1_DAY_RETURN: 0.0173347778981581
Predicted 2_DAY_RETURN: 0.0390032502708558
Predicted 7_DAY_RETURN: 57272192.0 |
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned. | TWEET: "RT @Reuters: Apple and Salesforce are entering a partnership that will bring Siri to more business apps. @stephennellis reports https://t.c…" STOCK: nan DATE: nan | Sentiment: (Provide sentiment here)
Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is nan and the TextBlob polarity score is nan. |
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends. | STOCK: nan 1_DAY_RETURN: nan 2_DAY_RETURN: nan 3_DAY_RETURN: nan 7_DAY_RETURN: nan | The stock shows a neutral return trend over the specified periods. |
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN. | STOCK: nan LAST_PRICE: nan PX_VOLUME: nan VOLATILITY_10D: nan VOLATILITY_30D: nan LSTM_POLARITY: nan TEXTBLOB_POLARITY: nan | Predicted 1_DAY_RETURN: nan
Predicted 2_DAY_RETURN: nan
Predicted 7_DAY_RETURN: nan |
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned. | TWEET: "Apple" STOCK: 27/09/2018 DATE: 224.95 | Sentiment: (Provide sentiment here)
Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is 0.5 and the TextBlob polarity score is @Reuters. |
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends. | STOCK: 27/09/2018 1_DAY_RETURN: -0.0122693931984885 2_DAY_RETURN: -0.0184929984440986 3_DAY_RETURN: -0.0218715270060012 7_DAY_RETURN: 30181227.0 | The stock shows a consistent positive return trend over the specified periods. |
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN. | STOCK: 27/09/2018 LAST_PRICE: -0.020137808401867 PX_VOLUME: 22.98 VOLATILITY_10D: 20.811 VOLATILITY_30D: -1.0 LSTM_POLARITY: 0.5 TEXTBLOB_POLARITY: @Reuters | Predicted 1_DAY_RETURN: -0.0122693931984885
Predicted 2_DAY_RETURN: -0.0184929984440986
Predicted 7_DAY_RETURN: 30181227.0 |
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned. | TWEET: "Check out Miami Heat fitted S / M baseball hat cap adidas red #adidas #MiamiHeat https://t.co/Vwm9JLoTnw via @eBay" STOCK: nan DATE: nan | Sentiment: (Provide sentiment here)
Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is nan and the TextBlob polarity score is nan. |
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends. | STOCK: nan 1_DAY_RETURN: nan 2_DAY_RETURN: nan 3_DAY_RETURN: nan 7_DAY_RETURN: nan | The stock shows a neutral return trend over the specified periods. |
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN. | STOCK: nan LAST_PRICE: nan PX_VOLUME: nan VOLATILITY_10D: nan VOLATILITY_30D: nan LSTM_POLARITY: nan TEXTBLOB_POLARITY: nan | Predicted 1_DAY_RETURN: nan
Predicted 2_DAY_RETURN: nan
Predicted 7_DAY_RETURN: nan |
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned. | TWEET: "adidas" STOCK: 27/09/2018 DATE: 211.0 | Sentiment: (Provide sentiment here)
Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is 0.0 and the TextBlob polarity score is @eBay. |
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends. | STOCK: 27/09/2018 1_DAY_RETURN: -0.0009478672985781 2_DAY_RETURN: -0.0123222748815165 3_DAY_RETURN: -0.0061611374407583 7_DAY_RETURN: 502120.0 | The stock shows a consistent positive return trend over the specified periods. |
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN. | STOCK: 27/09/2018 LAST_PRICE: -0.0123222748815165 PX_VOLUME: 18.618 VOLATILITY_10D: 15.553 VOLATILITY_30D: -1.0 LSTM_POLARITY: 0.0 TEXTBLOB_POLARITY: @eBay | Predicted 1_DAY_RETURN: -0.0009478672985781
Predicted 2_DAY_RETURN: -0.0123222748815165
Predicted 7_DAY_RETURN: 502120.0 |
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