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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: CBS received subpoenas from New York County District Attorney's Office and New York City Commission on Human Rights $CBS https…" 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: "CBS" STOCK: 28/09/2018 DATE: 57.45 | 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 0.0909090909090909 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: 28/09/2018 1_DAY_RETURN: -0.0165361183637946 2_DAY_RETURN: -0.0156657963446476 3_DAY_RETURN: -0.0123585726718886 7_DAY_RETURN: 2744527.0 | 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: 28/09/2018 LAST_PRICE: -0.0156657963446476 PX_VOLUME: 13.102 VOLATILITY_10D: 18.511 VOLATILITY_30D: 1.0 LSTM_POLARITY: 0.0909090909090909 TEXTBLOB_POLARITY: @Reuters | Predicted 1_DAY_RETURN: -0.0165361183637946
Predicted 2_DAY_RETURN: -0.0156657963446476
Predicted 7_DAY_RETURN: 2744527.0 |
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: UK regulator to enquire if Facebook data breach has affected UK citizens https://t.co/b0JJJK1rqs" 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: "Facebook" STOCK: 28/09/2018 DATE: 164.46 | 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 @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: 28/09/2018 1_DAY_RETURN: 0.0151404596862457 2_DAY_RETURN: 0.0027362276541407 3_DAY_RETURN: -0.0093031740240788 7_DAY_RETURN: 34265638.0 | 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: 28/09/2018 LAST_PRICE: 0.0266326158336373 PX_VOLUME: 26.211 VOLATILITY_10D: 23.132 VOLATILITY_30D: -1.0 LSTM_POLARITY: 0.0 TEXTBLOB_POLARITY: @Reuters | Predicted 1_DAY_RETURN: 0.0151404596862457
Predicted 2_DAY_RETURN: 0.0027362276541407
Predicted 7_DAY_RETURN: 34265638.0 |
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned. | TWEET: "@beingkaylara @ATT At least they didn't tell you that you were wrong about how you feel. Apple customer care rubbed… https://t.co/Gv9MI2t5Xm" 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: 28/09/2018 DATE: 225.74 | 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 @ATT. |
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends. | STOCK: 28/09/2018 1_DAY_RETURN: -0.0235669354124214 2_DAY_RETURN: -0.015726056525206 3_DAY_RETURN: -0.0357933906263843 7_DAY_RETURN: 22929364.0 | 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: 28/09/2018 LAST_PRICE: -0.0034996013112431 PX_VOLUME: 15.841 VOLATILITY_10D: 20.065 VOLATILITY_30D: 1.0 LSTM_POLARITY: -0.4 TEXTBLOB_POLARITY: @ATT | Predicted 1_DAY_RETURN: -0.0235669354124214
Predicted 2_DAY_RETURN: -0.015726056525206
Predicted 7_DAY_RETURN: 22929364.0 |
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned. | TWEET: "RT @SAP: In partnership with SAP, Vectus streamlined their operations and provides clean water to those who need it most. https://t.co/w5ba…
" STOCK: SAP DATE: 28/09/2018 | 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 0.43333333333333335. |
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends. | STOCK: SAP 1_DAY_RETURN: 0.014331510465774 2_DAY_RETURN: 0.0137657929473882 3_DAY_RETURN: 0.013388647935131 7_DAY_RETURN: -0.0220629832170469 | 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: SAP LAST_PRICE: 106.06 PX_VOLUME: 3068964.0 VOLATILITY_10D: 24.43 VOLATILITY_30D: 19.611 LSTM_POLARITY: -1 TEXTBLOB_POLARITY: 0.43333333333333335 | Predicted 1_DAY_RETURN: 0.014331510465774
Predicted 2_DAY_RETURN: 0.0137657929473882
Predicted 7_DAY_RETURN: -0.0220629832170469 |
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned. | TWEET: "@facebook It's Facebook..... https://t.co/2v61knwsak" 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: "Facebook" STOCK: 28/09/2018 DATE: 164.46 | 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 @facebook. |
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends. | STOCK: 28/09/2018 1_DAY_RETURN: 0.0151404596862457 2_DAY_RETURN: 0.0027362276541407 3_DAY_RETURN: -0.0093031740240788 7_DAY_RETURN: 34265638.0 | 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: 28/09/2018 LAST_PRICE: 0.0266326158336373 PX_VOLUME: 26.211 VOLATILITY_10D: 23.132 VOLATILITY_30D: -1.0 LSTM_POLARITY: 0.0 TEXTBLOB_POLARITY: @facebook | Predicted 1_DAY_RETURN: 0.0151404596862457
Predicted 2_DAY_RETURN: 0.0027362276541407
Predicted 7_DAY_RETURN: 34265638.0 |
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned. | TWEET: "RT @sistercrow: @facebook It's because Facebook just sold the data of 50 million users and is calling it a security breach and figures its…" 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: "Facebook" STOCK: 28/09/2018 DATE: 164.46 | 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 @facebook. |
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends. | STOCK: 28/09/2018 1_DAY_RETURN: 0.0151404596862457 2_DAY_RETURN: 0.0027362276541407 3_DAY_RETURN: -0.0093031740240788 7_DAY_RETURN: 34265638.0 | 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: 28/09/2018 LAST_PRICE: 0.0266326158336373 PX_VOLUME: 26.211 VOLATILITY_10D: 23.132 VOLATILITY_30D: -1.0 LSTM_POLARITY: 0.0 TEXTBLOB_POLARITY: @facebook | Predicted 1_DAY_RETURN: 0.0151404596862457
Predicted 2_DAY_RETURN: 0.0027362276541407
Predicted 7_DAY_RETURN: 34265638.0 |
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned. | TWEET: "@Reuters Christine Blasey Ford did more than that: She's telling human beings that they are more than just what th… https://t.co/sSVug4NIk9" 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: 28/09/2018 DATE: 9.25 | 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 0.3333333333333333 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: 28/09/2018 1_DAY_RETURN: 0.0021621621621621 2_DAY_RETURN: 0.0151351351351351 3_DAY_RETURN: 0.0648648648648648 7_DAY_RETURN: 30987233.0 | 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: 28/09/2018 LAST_PRICE: -0.0021621621621621 PX_VOLUME: 23.492 VOLATILITY_10D: 22.989 VOLATILITY_30D: -1.0 LSTM_POLARITY: 0.3333333333333333 TEXTBLOB_POLARITY: @Reuters | Predicted 1_DAY_RETURN: 0.0021621621621621
Predicted 2_DAY_RETURN: 0.0151351351351351
Predicted 7_DAY_RETURN: 30987233.0 |
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned. | TWEET: "This is what help @facebook is
My daughter reports to Facebook her account is hacked and gets this back
Thanks fac… https://t.co/MEVm1TI8Mt
" STOCK: Facebook DATE: 29/09/2018 | 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: Facebook 1_DAY_RETURN: 0.0 2_DAY_RETURN: 0.0266326158336373 3_DAY_RETURN: 0.0151404596862457 7_DAY_RETURN: -0.0093031740240788 | 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: Facebook LAST_PRICE: 164.46 PX_VOLUME: 34265638.0 VOLATILITY_10D: 26.211 VOLATILITY_30D: 23.132 LSTM_POLARITY: -1 TEXTBLOB_POLARITY: 0.0 | Predicted 1_DAY_RETURN: 0.0
Predicted 2_DAY_RETURN: 0.0266326158336373
Predicted 7_DAY_RETURN: -0.0093031740240788 |
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: Attorney Amal Clooney, Reuters Editor-in-Chief Steve Adler and the Committee to Protect Journalists held a panel on global pre…
" STOCK: Reuters DATE: 29/09/2018 | 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: Reuters 1_DAY_RETURN: 0.0 2_DAY_RETURN: 0.0008764973496391 3_DAY_RETURN: -0.0050343940740033 7_DAY_RETURN: -0.0067854012509465 | 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: Reuters LAST_PRICE: 50.3139 PX_VOLUME: 7988967.0 VOLATILITY_10D: 6.837999999999999 VOLATILITY_30D: 12.771 LSTM_POLARITY: 1 TEXTBLOB_POLARITY: 0.0 | Predicted 1_DAY_RETURN: 0.0
Predicted 2_DAY_RETURN: 0.0008764973496391
Predicted 7_DAY_RETURN: -0.0067854012509465 |
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: Attorney Amal Clooney, Reuters Editor-in-Chief Steve Adler and the Committee to Protect Journalists held a panel on global pre…
" STOCK: Reuters DATE: 29/09/2018 | 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: Reuters 1_DAY_RETURN: 0.0 2_DAY_RETURN: 0.0008764973496391 3_DAY_RETURN: -0.0050343940740033 7_DAY_RETURN: -0.0067854012509465 | 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: Reuters LAST_PRICE: 50.3139 PX_VOLUME: 7988967.0 VOLATILITY_10D: 6.837999999999999 VOLATILITY_30D: 12.771 LSTM_POLARITY: 1 TEXTBLOB_POLARITY: 0.0 | Predicted 1_DAY_RETURN: 0.0
Predicted 2_DAY_RETURN: 0.0008764973496391
Predicted 7_DAY_RETURN: -0.0067854012509465 |
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned. | TWEET: "I will love unboxing the mystery gems to reveal a Disney princess!! Check out the NEW @Disney Princess Gem Collecti… https://t.co/WMCeBB2GdI" 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: "Disney" STOCK: 29/09/2018 DATE: 116.94 | 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 0.78125 and the TextBlob polarity score is @Disney. |
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends. | STOCK: 29/09/2018 1_DAY_RETURN: -0.0076962544894817 2_DAY_RETURN: -0.0147939114075594 3_DAY_RETURN: -0.0559261159569009 7_DAY_RETURN: 7366846.0 | 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: 29/09/2018 LAST_PRICE: 0.0 PX_VOLUME: 15.232 VOLATILITY_10D: 13.23 VOLATILITY_30D: 1.0 LSTM_POLARITY: 0.78125 TEXTBLOB_POLARITY: @Disney | Predicted 1_DAY_RETURN: -0.0076962544894817
Predicted 2_DAY_RETURN: -0.0147939114075594
Predicted 7_DAY_RETURN: 7366846.0 |
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 Hot Wheels - '49 Ford C.O.E. - 2011 - Nostalgic Brands - Masters of the Universe https://t.co/o40ABG1FAH 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: "Ford" STOCK: 29/09/2018 DATE: 9.25 | 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 0.25 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: 29/09/2018 1_DAY_RETURN: -0.0021621621621621 2_DAY_RETURN: 0.0021621621621621 3_DAY_RETURN: 0.0648648648648648 7_DAY_RETURN: 30987233.0 | 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: 29/09/2018 LAST_PRICE: 0.0 PX_VOLUME: 23.492 VOLATILITY_10D: 22.989 VOLATILITY_30D: 1.0 LSTM_POLARITY: 0.25 TEXTBLOB_POLARITY: @eBay | Predicted 1_DAY_RETURN: -0.0021621621621621
Predicted 2_DAY_RETURN: 0.0021621621621621
Predicted 7_DAY_RETURN: 30987233.0 |
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned. | TWEET: "RT @Starbucks: @chocohyoo Hyungwon will always be our Starbucks king! 👑" 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: "Starbucks" STOCK: 29/09/2018 DATE: 56.84 | 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 @Starbucks. |
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends. | STOCK: 29/09/2018 1_DAY_RETURN: 0.0087966220971147 2_DAY_RETURN: 0.0075650950035186 3_DAY_RETURN: 0.0107318789584799 7_DAY_RETURN: 8975955.0 | 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: 29/09/2018 LAST_PRICE: 0.0 PX_VOLUME: 17.035 VOLATILITY_10D: 13.123 VOLATILITY_30D: 1.0 LSTM_POLARITY: 0.0 TEXTBLOB_POLARITY: @Starbucks | Predicted 1_DAY_RETURN: 0.0087966220971147
Predicted 2_DAY_RETURN: 0.0075650950035186
Predicted 7_DAY_RETURN: 8975955.0 |
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned. | TWEET: "Tim Cook visits Apple Store as new iPhones go on sale https://t.co/ZTbsygTg1q عبر @YouTube
@tim_cook @Apple
" STOCK: Apple DATE: 29/09/2018 | 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 0.13636363636363635. |
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends. | STOCK: Apple 1_DAY_RETURN: 0.0 2_DAY_RETURN: -0.0034996013112431 3_DAY_RETURN: -0.0235669354124214 7_DAY_RETURN: -0.0357933906263843 | 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: Apple LAST_PRICE: 225.74 PX_VOLUME: 22929364.0 VOLATILITY_10D: 15.841 VOLATILITY_30D: 20.065 LSTM_POLARITY: 1 TEXTBLOB_POLARITY: 0.13636363636363635 | Predicted 1_DAY_RETURN: 0.0
Predicted 2_DAY_RETURN: -0.0034996013112431
Predicted 7_DAY_RETURN: -0.0357933906263843 |
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned. | TWEET: "RT @PrivacyMatters: @MarcRotenberg @FTC @facebook @EPICprivacy Quite. Tick tock - it’s been two years since the Facebook WhatsApp privacy 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: "Facebook" STOCK: 29/09/2018 DATE: 164.46 | 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 @facebook. |
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends. | STOCK: 29/09/2018 1_DAY_RETURN: 0.0266326158336373 2_DAY_RETURN: 0.0151404596862457 3_DAY_RETURN: -0.0093031740240788 7_DAY_RETURN: 34265638.0 | 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: 29/09/2018 LAST_PRICE: 0.0 PX_VOLUME: 26.211 VOLATILITY_10D: 23.132 VOLATILITY_30D: -1.0 LSTM_POLARITY: 0.0 TEXTBLOB_POLARITY: @facebook | Predicted 1_DAY_RETURN: 0.0266326158336373
Predicted 2_DAY_RETURN: 0.0151404596862457
Predicted 7_DAY_RETURN: 34265638.0 |
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned. | TWEET: "RT @Chadwick_Moore: Before @snopes tries to discredit and smear my Ford story (remember they work with @facebook to throttle what they deem…" 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: 29/09/2018 DATE: 9.25 | 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 @facebook. |
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends. | STOCK: 29/09/2018 1_DAY_RETURN: -0.0021621621621621 2_DAY_RETURN: 0.0021621621621621 3_DAY_RETURN: 0.0648648648648648 7_DAY_RETURN: 30987233.0 | 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: 29/09/2018 LAST_PRICE: 0.0 PX_VOLUME: 23.492 VOLATILITY_10D: 22.989 VOLATILITY_30D: -1.0 LSTM_POLARITY: -0.1 TEXTBLOB_POLARITY: @facebook | Predicted 1_DAY_RETURN: -0.0021621621621621
Predicted 2_DAY_RETURN: 0.0021621621621621
Predicted 7_DAY_RETURN: 30987233.0 |
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: 'History will judge her on her response,' Amal Clooney on Myanmar's Aung San Suu Kyi and jailed Reuters reporters https://t.co…
" STOCK: Reuters DATE: 29/09/2018 | 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: Reuters 1_DAY_RETURN: 0.0 2_DAY_RETURN: 0.0008764973496391 3_DAY_RETURN: -0.0050343940740033 7_DAY_RETURN: -0.0067854012509465 | 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: Reuters LAST_PRICE: 50.3139 PX_VOLUME: 7988967.0 VOLATILITY_10D: 6.837999999999999 VOLATILITY_30D: 12.771 LSTM_POLARITY: 1 TEXTBLOB_POLARITY: 0.0 | Predicted 1_DAY_RETURN: 0.0
Predicted 2_DAY_RETURN: 0.0008764973496391
Predicted 7_DAY_RETURN: -0.0067854012509465 |
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned. | TWEET: "@Apple Lower your prices greedy Apple." 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: 29/09/2018 DATE: 225.74 | 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 @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: 29/09/2018 1_DAY_RETURN: -0.0034996013112431 2_DAY_RETURN: -0.0235669354124214 3_DAY_RETURN: -0.0357933906263843 7_DAY_RETURN: 22929364.0 | 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: 29/09/2018 LAST_PRICE: 0.0 PX_VOLUME: 15.841 VOLATILITY_10D: 20.065 VOLATILITY_30D: -1.0 LSTM_POLARITY: 0.0 TEXTBLOB_POLARITY: @Apple | Predicted 1_DAY_RETURN: -0.0034996013112431
Predicted 2_DAY_RETURN: -0.0235669354124214
Predicted 7_DAY_RETURN: 22929364.0 |
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 Nike USA World Cup Away Jersey Kid's Soccer Jersey Size Large (14-16) Used #Nike https://t.co/mfPo3duiMe 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: "Nike" STOCK: 29/09/2018 DATE: 84.72 | 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 0.21428571428571427 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: 29/09/2018 1_DAY_RETURN: -0.0021246458923511 2_DAY_RETURN: -0.0120396600566571 3_DAY_RETURN: 0.0097969782813975 7_DAY_RETURN: 7452735.0 | 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: 29/09/2018 LAST_PRICE: 0.0 PX_VOLUME: 20.47 VOLATILITY_10D: 20.016 VOLATILITY_30D: -1.0 LSTM_POLARITY: 0.21428571428571427 TEXTBLOB_POLARITY: @eBay | Predicted 1_DAY_RETURN: -0.0021246458923511
Predicted 2_DAY_RETURN: -0.0120396600566571
Predicted 7_DAY_RETURN: 7452735.0 |
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned. | TWEET: "RT @JanetCBrennan: OUT NOW!
Barnes & Noble, @BNBuzz: https://t.co/Mt5IV8mEgR
Amazon, @amazon : https://t.co/8b3Axxiri8 …
Kobo, @Kobo: ht…
" STOCK: Amazon DATE: 29/09/2018 | 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 0.6. |
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends. | STOCK: Amazon 1_DAY_RETURN: 0.0 2_DAY_RETURN: 0.0049825262106839 3_DAY_RETURN: -0.014053919121318 7_DAY_RETURN: -0.0439291063404892 | 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: Amazon LAST_PRICE: 2003.0 PX_VOLUME: 4085135.0 VOLATILITY_10D: 20.709 VOLATILITY_30D: 22.946 LSTM_POLARITY: 1 TEXTBLOB_POLARITY: 0.6 | Predicted 1_DAY_RETURN: 0.0
Predicted 2_DAY_RETURN: 0.0049825262106839
Predicted 7_DAY_RETURN: -0.0439291063404892 |
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned. | TWEET: "Just saw this on Amazon: Crocs Kids' Boys & Girls Handle It Rain Boot by Crocs https://t.co/1Tj0v7Ge64 via @amazon" 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. |
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