instruction
stringclasses 3
values | input
stringlengths 33
307
| output
stringlengths 66
248
|
|---|---|---|
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: "Netflix" STOCK: 26/09/2018 DATE: 377.88 | 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 @netflix. |
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends. | STOCK: 26/09/2018 1_DAY_RETURN: -0.0218852545781729 2_DAY_RETURN: -0.0441674605694929 3_DAY_RETURN: -0.0288980628771038 7_DAY_RETURN: 13799728.0 | 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: 26/09/2018 LAST_PRICE: -0.0223615962739493 PX_VOLUME: 40.798 VOLATILITY_10D: 40.576 VOLATILITY_30D: 1.0 LSTM_POLARITY: 0.5 TEXTBLOB_POLARITY: @netflix | Predicted 1_DAY_RETURN: -0.0218852545781729
Predicted 2_DAY_RETURN: -0.0441674605694929
Predicted 7_DAY_RETURN: 13799728.0 |
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned. | TWEET: "RT @Microsoft: Announced at #MSIgnite: @Adobe, @SAP, and Microsoft announced the Open Data Initiative, which will enable data to be exchang…
" STOCK: Microsoft DATE: 26/09/2018 | 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: Microsoft 1_DAY_RETURN: 0.0041235304439375 2_DAY_RETURN: 0.0060536936304614 3_DAY_RETURN: 0.0024565713283032 7_DAY_RETURN: -0.0200035093876118 | 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: Microsoft LAST_PRICE: 113.98 PX_VOLUME: 19352025.0 VOLATILITY_10D: 15.528 VOLATILITY_30D: 16.141 LSTM_POLARITY: -1 TEXTBLOB_POLARITY: 0.0 | Predicted 1_DAY_RETURN: 0.0041235304439375
Predicted 2_DAY_RETURN: 0.0060536936304614
Predicted 7_DAY_RETURN: -0.0200035093876118 |
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 Sony PS2 Systems One Working One Parts Repair Hook Ups Controllers Memory Cards #Sony https://t.co/8YJuK3tfvM 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: "Sony" STOCK: 26/09/2018 DATE: 6768.0 | 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: 26/09/2018 1_DAY_RETURN: -0.0478723404255319 2_DAY_RETURN: -0.0478723404255319 3_DAY_RETURN: -0.0127068557919621 7_DAY_RETURN: 8840400.0 | 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: 26/09/2018 LAST_PRICE: -0.0162529550827423 PX_VOLUME: 30.754 VOLATILITY_10D: 21.151 VOLATILITY_30D: 1.0 LSTM_POLARITY: 0.0 TEXTBLOB_POLARITY: @eBay | Predicted 1_DAY_RETURN: -0.0478723404255319
Predicted 2_DAY_RETURN: -0.0478723404255319
Predicted 7_DAY_RETURN: 8840400.0 |
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned. | TWEET: "RT @NobodyListens1: @michellemalkin @Google Yep my child was given a Google Chromebook, a Gmail account and a Google Classroom login withou…
" STOCK: Google DATE: 26/09/2018 | 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: Google 1_DAY_RETURN: -0.0001423714051218 2_DAY_RETURN: -0.0121434433780547 3_DAY_RETURN: -0.0183742860492773 7_DAY_RETURN: -0.0165737065139104 | 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: Google LAST_PRICE: 1194.06 PX_VOLUME: 1882524.0 VOLATILITY_10D: 17.933 VOLATILITY_30D: 17.414 LSTM_POLARITY: -1 TEXTBLOB_POLARITY: 0.0 | Predicted 1_DAY_RETURN: -0.0001423714051218
Predicted 2_DAY_RETURN: -0.0121434433780547
Predicted 7_DAY_RETURN: -0.0165737065139104 |
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: SAP HANA is helping @ElephantsRhinos & People (ERP) preserve the world's ecosystem for future generations. https://t.co/H8kclZW3lr…" 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: "SAP" STOCK: 26/09/2018 DATE: 107.52 | 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 @SAP. |
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends. | STOCK: 26/09/2018 1_DAY_RETURN: -0.0364583333333333 2_DAY_RETURN: -0.0353422619047618 3_DAY_RETURN: -0.0470610119047619 7_DAY_RETURN: 2455839.0 | 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: 26/09/2018 LAST_PRICE: -0.0003720238095237 PX_VOLUME: 25.281 VOLATILITY_10D: 19.113 VOLATILITY_30D: -1.0 LSTM_POLARITY: 0.0 TEXTBLOB_POLARITY: @SAP | Predicted 1_DAY_RETURN: -0.0364583333333333
Predicted 2_DAY_RETURN: -0.0353422619047618
Predicted 7_DAY_RETURN: 2455839.0 |
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned. | TWEET: "@CeeReedy @mmcintire @pfizer @Honda @subaru_usa I just bought a brand new Honda CR-V. I see I made a good choice." 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: "Honda" STOCK: 26/09/2018 DATE: 3427.0 | 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 0.13636363636363635 and the TextBlob polarity score is @pfizer. |
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends. | STOCK: 26/09/2018 1_DAY_RETURN: 0.0186752261453166 2_DAY_RETURN: 0.0186752261453166 3_DAY_RETURN: -0.0128392179749051 7_DAY_RETURN: 6281500.0 | 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: 26/09/2018 LAST_PRICE: 0.0259702363583309 PX_VOLUME: 29.988000000000003 VOLATILITY_10D: 23.171 VOLATILITY_30D: 1.0 LSTM_POLARITY: 0.13636363636363635 TEXTBLOB_POLARITY: @pfizer | Predicted 1_DAY_RETURN: 0.0186752261453166
Predicted 2_DAY_RETURN: 0.0186752261453166
Predicted 7_DAY_RETURN: 6281500.0 |
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 Sony Playstation One ☆ PS1 ☆ Console ONLY ☆ For Parts Only ☆ NOT WORKING #Sony https://t.co/vDKloUsk2a 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: "Sony" STOCK: 26/09/2018 DATE: 6768.0 | 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: 26/09/2018 1_DAY_RETURN: -0.0478723404255319 2_DAY_RETURN: -0.0478723404255319 3_DAY_RETURN: -0.0127068557919621 7_DAY_RETURN: 8840400.0 | 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: 26/09/2018 LAST_PRICE: -0.0162529550827423 PX_VOLUME: 30.754 VOLATILITY_10D: 21.151 VOLATILITY_30D: -1.0 LSTM_POLARITY: 0.0 TEXTBLOB_POLARITY: @eBay | Predicted 1_DAY_RETURN: -0.0478723404255319
Predicted 2_DAY_RETURN: -0.0478723404255319
Predicted 7_DAY_RETURN: 8840400.0 |
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned. | TWEET: "RT @NobodyListens1: @michellemalkin @Google Yep my child was given a Google Chromebook, a Gmail account and a Google Classroom login withou…
" STOCK: Google DATE: 26/09/2018 | 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: Google 1_DAY_RETURN: -0.0001423714051218 2_DAY_RETURN: -0.0121434433780547 3_DAY_RETURN: -0.0183742860492773 7_DAY_RETURN: -0.0165737065139104 | 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: Google LAST_PRICE: 1194.06 PX_VOLUME: 1882524.0 VOLATILITY_10D: 17.933 VOLATILITY_30D: 17.414 LSTM_POLARITY: -1 TEXTBLOB_POLARITY: 0.0 | Predicted 1_DAY_RETURN: -0.0001423714051218
Predicted 2_DAY_RETURN: -0.0121434433780547
Predicted 7_DAY_RETURN: -0.0165737065139104 |
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned. | TWEET: "Excited to see Jacksonville, Orlando and Tampa on this list! @CNBC @amazon
Amazon just rolled out its Whole Foods… https://t.co/gQUFi7KWvH
" STOCK: Amazon DATE: 26/09/2018 | 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 0.46875. |
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: -0.0001519102716661 2_DAY_RETURN: -0.0205028229992151 3_DAY_RETURN: -0.0303010355216851 7_DAY_RETURN: -0.0245233815226472 | 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: 1974.85 PX_VOLUME: 4313459.0 VOLATILITY_10D: 27.409 VOLATILITY_30D: 22.276 LSTM_POLARITY: -1 TEXTBLOB_POLARITY: 0.46875 | Predicted 1_DAY_RETURN: -0.0001519102716661
Predicted 2_DAY_RETURN: -0.0205028229992151
Predicted 7_DAY_RETURN: -0.0245233815226472 |
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 Sony SRS-XB10 Portable Bluetooth NFC Wireless Speaker 🔊 $24.99 https://t.co/Dz2DCVrHmL @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: "Sony" STOCK: 26/09/2018 DATE: 6768.0 | 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: 26/09/2018 1_DAY_RETURN: -0.0478723404255319 2_DAY_RETURN: -0.0478723404255319 3_DAY_RETURN: -0.0127068557919621 7_DAY_RETURN: 8840400.0 | 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: 26/09/2018 LAST_PRICE: -0.0162529550827423 PX_VOLUME: 30.754 VOLATILITY_10D: 21.151 VOLATILITY_30D: 1.0 LSTM_POLARITY: 0.0 TEXTBLOB_POLARITY: @eBay | Predicted 1_DAY_RETURN: -0.0478723404255319
Predicted 2_DAY_RETURN: -0.0478723404255319
Predicted 7_DAY_RETURN: 8840400.0 |
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned. | TWEET: "You can pre-order the book on Amazon.
My Sister, the Serial Killer: A Novel by Oyinkan Braithwaite https://t.co/rf2J462L93 via @amazon
" STOCK: Amazon DATE: 26/09/2018 | 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: Amazon 1_DAY_RETURN: -0.0001519102716661 2_DAY_RETURN: -0.0205028229992151 3_DAY_RETURN: -0.0303010355216851 7_DAY_RETURN: -0.0245233815226472 | 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: 1974.85 PX_VOLUME: 4313459.0 VOLATILITY_10D: 27.409 VOLATILITY_30D: 22.276 LSTM_POLARITY: 1 TEXTBLOB_POLARITY: 0.0 | Predicted 1_DAY_RETURN: -0.0001519102716661
Predicted 2_DAY_RETURN: -0.0205028229992151
Predicted 7_DAY_RETURN: -0.0245233815226472 |
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 Vintage Burberry Hats Lot Of 2 #Burberry https://t.co/JknBTQxuCo 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: "Burberry" STOCK: 26/09/2018 DATE: 2015.0 | 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: 26/09/2018 1_DAY_RETURN: -0.001985111662531 2_DAY_RETURN: -0.0014888337468982 3_DAY_RETURN: 0.0476426799007444 7_DAY_RETURN: 1136331.0 | 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: 26/09/2018 LAST_PRICE: -0.0096774193548387 PX_VOLUME: 37.103 VOLATILITY_10D: 29.45 VOLATILITY_30D: -1.0 LSTM_POLARITY: 0.0 TEXTBLOB_POLARITY: @eBay | Predicted 1_DAY_RETURN: -0.001985111662531
Predicted 2_DAY_RETURN: -0.0014888337468982
Predicted 7_DAY_RETURN: 1136331.0 |
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned. | TWEET: "@brizzledan @MichaelJaiWhite @netflix Not sure. Hey, @NetflixUK, is Black Dynamite coming to Netflix there in October?
" STOCK: Netflix DATE: 26/09/2018 | 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 -0.25. |
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends. | STOCK: Netflix 1_DAY_RETURN: -0.0223615962739493 2_DAY_RETURN: -0.0218852545781729 3_DAY_RETURN: -0.0441674605694929 7_DAY_RETURN: -0.0288980628771038 | 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: Netflix LAST_PRICE: 377.88 PX_VOLUME: 13799728.0 VOLATILITY_10D: 40.798 VOLATILITY_30D: 40.576 LSTM_POLARITY: 1 TEXTBLOB_POLARITY: -0.25 | Predicted 1_DAY_RETURN: -0.0223615962739493
Predicted 2_DAY_RETURN: -0.0218852545781729
Predicted 7_DAY_RETURN: -0.0288980628771038 |
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned. | TWEET: "@fox5melanie @facebook @fox5dc What does it mean? Been There Done Your Z ..?
And who went on his Facebook page afte… https://t.co/qOHiFGrMRs
" STOCK: Facebook DATE: 26/09/2018 | 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 -0.3125. |
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: -0.0122192273135668 2_DAY_RETURN: -0.0092243186582808 3_DAY_RETURN: -0.0240790655884994 7_DAY_RETURN: -0.0233003893381251 | 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: 166.95 PX_VOLUME: 25252231.0 VOLATILITY_10D: 21.19400000000001 VOLATILITY_30D: 22.882 LSTM_POLARITY: -1 TEXTBLOB_POLARITY: -0.3125 | Predicted 1_DAY_RETURN: -0.0122192273135668
Predicted 2_DAY_RETURN: -0.0092243186582808
Predicted 7_DAY_RETURN: -0.0233003893381251 |
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: Trump metals tariffs will cost Ford $1 billion in profits, CEO says https://t.co/fhKmVqOLoe
" STOCK: Ford DATE: 26/09/2018 | 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: Ford 1_DAY_RETURN: 0.0129449838187703 2_DAY_RETURN: 0.0345199568500539 3_DAY_RETURN: 0.0625674217907227 7_DAY_RETURN: 0.0550161812297734 | 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: 9.27 PX_VOLUME: 58597487.0 VOLATILITY_10D: 25.227 VOLATILITY_30D: 23.103 LSTM_POLARITY: -1 TEXTBLOB_POLARITY: 0.0 | Predicted 1_DAY_RETURN: 0.0129449838187703
Predicted 2_DAY_RETURN: 0.0345199568500539
Predicted 7_DAY_RETURN: 0.0550161812297734 |
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: Trump metals tariffs will cost Ford $1 billion in profits, CEO says https://t.co/fhKmVqOLoe
" STOCK: Ford DATE: 26/09/2018 | 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: Ford 1_DAY_RETURN: 0.0129449838187703 2_DAY_RETURN: 0.0345199568500539 3_DAY_RETURN: 0.0625674217907227 7_DAY_RETURN: 0.0550161812297734 | 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: 9.27 PX_VOLUME: 58597487.0 VOLATILITY_10D: 25.227 VOLATILITY_30D: 23.103 LSTM_POLARITY: -1 TEXTBLOB_POLARITY: 0.0 | Predicted 1_DAY_RETURN: 0.0129449838187703
Predicted 2_DAY_RETURN: 0.0345199568500539
Predicted 7_DAY_RETURN: 0.0550161812297734 |
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned. | TWEET: "RT @49ers: Our friends at @arco and @Toyota want to put one lucky fan in this via the TOP TIER™️ Fan Toyota Truck Sweepstakes!
Enter here:…
" STOCK: Toyota DATE: 26/09/2018 | 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 0.4166666666666666. |
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends. | STOCK: Toyota 1_DAY_RETURN: 0.0130867709815078 2_DAY_RETURN: -0.0042674253200568 3_DAY_RETURN: -0.0042674253200568 7_DAY_RETURN: -0.0015647226173541 | 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: Toyota LAST_PRICE: 7030.0 PX_VOLUME: 7999900.0 VOLATILITY_10D: 18.397 VOLATILITY_30D: 18.343 LSTM_POLARITY: -1 TEXTBLOB_POLARITY: 0.4166666666666666 | Predicted 1_DAY_RETURN: 0.0130867709815078
Predicted 2_DAY_RETURN: -0.0042674253200568
Predicted 7_DAY_RETURN: -0.0015647226173541 |
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned. | TWEET: "RT @Bosslogic: Marvel's Night Nurse (only on Disney Play)
"sometimes heroes need saving" @rosariodawson
Think about if @Disney you can hav…
" STOCK: Disney DATE: 26/09/2018 | 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: Disney 1_DAY_RETURN: -0.0137140873188091 2_DAY_RETURN: -0.0211787171252495 3_DAY_RETURN: -0.0417498481034631 7_DAY_RETURN: -0.0470445273847755 | 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: Disney LAST_PRICE: 115.21 PX_VOLUME: 11141544.0 VOLATILITY_10D: 18.798 VOLATILITY_30D: 13.007 LSTM_POLARITY: 1 TEXTBLOB_POLARITY: 0.0 | Predicted 1_DAY_RETURN: -0.0137140873188091
Predicted 2_DAY_RETURN: -0.0211787171252495
Predicted 7_DAY_RETURN: -0.0470445273847755 |
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned. | TWEET: "@ChuckRobbins @Webex @Cisco A 36 hour outage!
I find it hard to believe that a Company as large as Cisco had no DR or redundancy in place
" STOCK: Cisco DATE: 26/09/2018 | 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 -0.038690476190476206. |
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends. | STOCK: Cisco 1_DAY_RETURN: 0.0012394133443503 2_DAY_RETURN: 0.0006197066721751 3_DAY_RETURN: 0.0030985333608759 7_DAY_RETURN: -0.0233422846519313 | 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: Cisco LAST_PRICE: 48.41 PX_VOLUME: 20080051.0 VOLATILITY_10D: 12.05 VOLATILITY_30D: 13.992 LSTM_POLARITY: 1 TEXTBLOB_POLARITY: -0.038690476190476206 | Predicted 1_DAY_RETURN: 0.0012394133443503
Predicted 2_DAY_RETURN: 0.0006197066721751
Predicted 7_DAY_RETURN: -0.0233422846519313 |
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned. | TWEET: "@Reuters Don’t worry the fools at Ford Field watching the Lions will make up the difference in beer sales alone! Ke… https://t.co/PHhUESjD6I" 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: 26/09/2018 DATE: 9.27 | 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: 26/09/2018 1_DAY_RETURN: 0.0345199568500539 2_DAY_RETURN: 0.0625674217907227 3_DAY_RETURN: 0.0550161812297734 7_DAY_RETURN: 58597487.0 | 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: 26/09/2018 LAST_PRICE: 0.0129449838187703 PX_VOLUME: 25.227 VOLATILITY_10D: 23.103 VOLATILITY_30D: -1.0 LSTM_POLARITY: 0.0 TEXTBLOB_POLARITY: @Reuters | Predicted 1_DAY_RETURN: 0.0345199568500539
Predicted 2_DAY_RETURN: 0.0625674217907227
Predicted 7_DAY_RETURN: 58597487.0 |
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: 26/09/2018 DATE: 57.27 | 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: 26/09/2018 1_DAY_RETURN: -0.0101274663872883 2_DAY_RETURN: 0.003143006809848 3_DAY_RETURN: -0.0321285140562249 7_DAY_RETURN: 7757332.0 | 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: 26/09/2018 LAST_PRICE: -0.0064606251091322 PX_VOLUME: 16.29 VOLATILITY_10D: 12.894 VOLATILITY_30D: 1.0 LSTM_POLARITY: 0.0 TEXTBLOB_POLARITY: @Starbucks | Predicted 1_DAY_RETURN: -0.0101274663872883
Predicted 2_DAY_RETURN: 0.003143006809848
Predicted 7_DAY_RETURN: 7757332.0 |
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: Ford Motor Chief Executive Officer James Hackett said that metals tariffs are costing the carmaker $1 billion. https://t.co/Uz…" 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: 26/09/2018 DATE: 9.27 | 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: 26/09/2018 1_DAY_RETURN: 0.0345199568500539 2_DAY_RETURN: 0.0625674217907227 3_DAY_RETURN: 0.0550161812297734 7_DAY_RETURN: 58597487.0 | 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: 26/09/2018 LAST_PRICE: 0.0129449838187703 PX_VOLUME: 25.227 VOLATILITY_10D: 23.103 VOLATILITY_30D: -1.0 LSTM_POLARITY: 0.0 TEXTBLOB_POLARITY: @Reuters | Predicted 1_DAY_RETURN: 0.0345199568500539
Predicted 2_DAY_RETURN: 0.0625674217907227
Predicted 7_DAY_RETURN: 58597487.0 |
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned. | TWEET: "RT @45StableGenius: @Reuters To all the trump voters who work at Ford or own Ford stock..."thoughts and prayers" is all I got for you. Oh,…
" STOCK: Ford DATE: 26/09/2018 | 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: Ford 1_DAY_RETURN: 0.0129449838187703 2_DAY_RETURN: 0.0345199568500539 3_DAY_RETURN: 0.0625674217907227 7_DAY_RETURN: 0.0550161812297734 | 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: 9.27 PX_VOLUME: 58597487.0 VOLATILITY_10D: 25.227 VOLATILITY_30D: 23.103 LSTM_POLARITY: -1 TEXTBLOB_POLARITY: 0.6 | Predicted 1_DAY_RETURN: 0.0129449838187703
Predicted 2_DAY_RETURN: 0.0345199568500539
Predicted 7_DAY_RETURN: 0.0550161812297734 |
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 Disney The Jungle Book 1955 Chevy Panel 3+ New #Mattel #Chevrolet https://t.co/wLwHPi3LF9 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 |
Subsets and Splits