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Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN.
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STOCK: 01/02/2017 LAST_PRICE: -0.0108763206960845 PX_VOLUME: 33.037 VOLATILITY_10D: 22.838 VOLATILITY_30D: 1.0 LSTM_POLARITY: 0.0 TEXTBLOB_POLARITY: @eBay
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Predicted 1_DAY_RETURN: -0.000310752019888
Predicted 2_DAY_RETURN: 0.0102548166563082
Predicted 7_DAY_RETURN: 9700776.0
|
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
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TWEET: "@cultcommoncore @Starbucks Exactly how nonracist people feel about you not going to Starbucks." STOCK: nan DATE: nan
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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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Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends.
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STOCK: nan 1_DAY_RETURN: nan 2_DAY_RETURN: nan 3_DAY_RETURN: nan 7_DAY_RETURN: nan
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The stock shows a neutral return trend over the specified periods.
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Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN.
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STOCK: nan LAST_PRICE: nan PX_VOLUME: nan VOLATILITY_10D: nan VOLATILITY_30D: nan LSTM_POLARITY: nan TEXTBLOB_POLARITY: nan
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Predicted 1_DAY_RETURN: nan
Predicted 2_DAY_RETURN: nan
Predicted 7_DAY_RETURN: nan
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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.
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TWEET: "Starbucks" STOCK: 01/02/2017 DATE: 53.9
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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 @Starbucks.
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Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends.
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STOCK: 01/02/2017 1_DAY_RETURN: 0.0371057513914656 2_DAY_RETURN: 0.0411873840445268 3_DAY_RETURN: 0.0890538033395177 7_DAY_RETURN: 18796871.0
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The stock shows a consistent positive return trend over the specified periods.
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Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN.
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STOCK: 01/02/2017 LAST_PRICE: 0.0244897959183673 PX_VOLUME: 25.781 VOLATILITY_10D: 18.576 VOLATILITY_30D: -1.0 LSTM_POLARITY: 0.25 TEXTBLOB_POLARITY: @Starbucks
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Predicted 1_DAY_RETURN: 0.0371057513914656
Predicted 2_DAY_RETURN: 0.0411873840445268
Predicted 7_DAY_RETURN: 18796871.0
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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.
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TWEET: "RT @trvo512: are you fucking kidding me? @McDonalds https://t.co/YgNCoLqJhr" STOCK: nan DATE: nan
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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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Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends.
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STOCK: nan 1_DAY_RETURN: nan 2_DAY_RETURN: nan 3_DAY_RETURN: nan 7_DAY_RETURN: nan
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The stock shows a neutral return trend over the specified periods.
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Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN.
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STOCK: nan LAST_PRICE: nan PX_VOLUME: nan VOLATILITY_10D: nan VOLATILITY_30D: nan LSTM_POLARITY: nan TEXTBLOB_POLARITY: nan
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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.
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TWEET: "McDonald's" STOCK: 01/02/2017 DATE: 122.42
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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 -0.6 and the TextBlob polarity score is @McDonalds.
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Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends.
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STOCK: 01/02/2017 1_DAY_RETURN: 0.004901159941186 2_DAY_RETURN: 0.0035941839568697 3_DAY_RETURN: -0.0051462179382453 7_DAY_RETURN: 3233576.0
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The stock shows a consistent positive return trend over the specified periods.
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Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN.
|
STOCK: 01/02/2017 LAST_PRICE: 0.0012252899852964 PX_VOLUME: 7.607 VOLATILITY_10D: 9.573 VOLATILITY_30D: -1.0 LSTM_POLARITY: -0.6 TEXTBLOB_POLARITY: @McDonalds
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Predicted 1_DAY_RETURN: 0.004901159941186
Predicted 2_DAY_RETURN: 0.0035941839568697
Predicted 7_DAY_RETURN: 3233576.0
|
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
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TWEET: "RT @CieloChulacielo: @netflix #BATB Would rather watch this show than any other #NetflixSaveBatB https://t.co/xrRrDKftWk" STOCK: nan DATE: nan
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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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Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends.
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STOCK: nan 1_DAY_RETURN: nan 2_DAY_RETURN: nan 3_DAY_RETURN: nan 7_DAY_RETURN: nan
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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
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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: 01/02/2017 DATE: 140.78
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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 -0.125 and the TextBlob polarity score is @netflix.
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Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends.
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STOCK: 01/02/2017 1_DAY_RETURN: 0.0031254439551072 2_DAY_RETURN: 0.0118624804659751 3_DAY_RETURN: -0.0089501349623525 7_DAY_RETURN: 6033422.0
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The stock shows a consistent positive return trend over the specified periods.
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Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN.
|
STOCK: 01/02/2017 LAST_PRICE: -0.0004972297201306 PX_VOLUME: 19.491 VOLATILITY_10D: 24.09800000000001 VOLATILITY_30D: 1.0 LSTM_POLARITY: -0.125 TEXTBLOB_POLARITY: @netflix
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Predicted 1_DAY_RETURN: 0.0031254439551072
Predicted 2_DAY_RETURN: 0.0118624804659751
Predicted 7_DAY_RETURN: 6033422.0
|
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
|
TWEET: "RT @LornajaneCosign: Randolph Duke The Look Sleeveless Leopard Print Lined Blouse #PlusSize 2X #Shopping https://t.co/j1vpF4hO8e 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.
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STOCK: nan 1_DAY_RETURN: nan 2_DAY_RETURN: nan 3_DAY_RETURN: nan 7_DAY_RETURN: nan
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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: "eBay" STOCK: 01/02/2017 DATE: 32.18
|
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: 01/02/2017 1_DAY_RETURN: -0.000310752019888 2_DAY_RETURN: 0.0102548166563082 3_DAY_RETURN: -0.0605966438781851 7_DAY_RETURN: 9700776.0
|
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: 01/02/2017 LAST_PRICE: -0.0108763206960845 PX_VOLUME: 33.037 VOLATILITY_10D: 22.838 VOLATILITY_30D: -1.0 LSTM_POLARITY: 0.0 TEXTBLOB_POLARITY: @eBay
|
Predicted 1_DAY_RETURN: -0.000310752019888
Predicted 2_DAY_RETURN: 0.0102548166563082
Predicted 7_DAY_RETURN: 9700776.0
|
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
|
TWEET: "RT @trvo512: are you fucking kidding me? @McDonalds https://t.co/YgNCoLqJhr" 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.
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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: "McDonald's" STOCK: 01/02/2017 DATE: 122.42
|
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 -0.6 and the TextBlob polarity score is @McDonalds.
|
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends.
|
STOCK: 01/02/2017 1_DAY_RETURN: 0.004901159941186 2_DAY_RETURN: 0.0035941839568697 3_DAY_RETURN: -0.0051462179382453 7_DAY_RETURN: 3233576.0
|
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: 01/02/2017 LAST_PRICE: 0.0012252899852964 PX_VOLUME: 7.607 VOLATILITY_10D: 9.573 VOLATILITY_30D: -1.0 LSTM_POLARITY: -0.6 TEXTBLOB_POLARITY: @McDonalds
|
Predicted 1_DAY_RETURN: 0.004901159941186
Predicted 2_DAY_RETURN: 0.0035941839568697
Predicted 7_DAY_RETURN: 3233576.0
|
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
|
TWEET: "RT @_SocSpecialist_: Seattle’sCityCouncil will vote Wed on bill that would take away $3BIL in city business from @WellsFargo, backer of #DA…
" STOCK: Wells Fargo DATE: 01/02/2017
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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.
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STOCK: Wells Fargo 1_DAY_RETURN: 0.0075120729744232 2_DAY_RETURN: 0.0030406009658379 3_DAY_RETURN: 0.0121624038633519 7_DAY_RETURN: 0.012698980504382
|
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: Wells Fargo LAST_PRICE: 55.91 PX_VOLUME: 21157698.0 VOLATILITY_10D: 24.594 VOLATILITY_30D: 20.199 LSTM_POLARITY: 1 TEXTBLOB_POLARITY: 0.0
|
Predicted 1_DAY_RETURN: 0.0075120729744232
Predicted 2_DAY_RETURN: 0.0030406009658379
Predicted 7_DAY_RETURN: 0.012698980504382
|
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
|
TWEET: "@JohnLegere @TMobile I would just love for once to not be continually screwed every month on my bill that you guys keep doing to me." STOCK: nan DATE: nan
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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
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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: "TMobile" STOCK: 01/02/2017 DATE: 63.26
|
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 @TMobile.
|
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends.
|
STOCK: 01/02/2017 1_DAY_RETURN: -0.0336705659184317 2_DAY_RETURN: -0.0132785330382547 3_DAY_RETURN: -0.0398355991147644 7_DAY_RETURN: 8937496.0
|
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: 01/02/2017 LAST_PRICE: -0.0156496996522288 PX_VOLUME: 31.18 VOLATILITY_10D: 25.996 VOLATILITY_30D: -1.0 LSTM_POLARITY: 0.5 TEXTBLOB_POLARITY: @TMobile
|
Predicted 1_DAY_RETURN: -0.0336705659184317
Predicted 2_DAY_RETURN: -0.0132785330382547
Predicted 7_DAY_RETURN: 8937496.0
|
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 Halloween Budget Life-Size Skull (Fourth Quality) #APPLAM https://t.co/Zrjf8yn3C4 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
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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: "eBay" STOCK: 01/02/2017 DATE: 32.18
|
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: 01/02/2017 1_DAY_RETURN: -0.000310752019888 2_DAY_RETURN: 0.0102548166563082 3_DAY_RETURN: -0.0605966438781851 7_DAY_RETURN: 9700776.0
|
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: 01/02/2017 LAST_PRICE: -0.0108763206960845 PX_VOLUME: 33.037 VOLATILITY_10D: 22.838 VOLATILITY_30D: 1.0 LSTM_POLARITY: 0.0 TEXTBLOB_POLARITY: @eBay
|
Predicted 1_DAY_RETURN: -0.000310752019888
Predicted 2_DAY_RETURN: 0.0102548166563082
Predicted 7_DAY_RETURN: 9700776.0
|
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
|
TWEET: "@Reuters this monster is going to undo any positive ground we've covered in 50 years. No stone unturned looking to be destructive." STOCK: nan DATE: nan
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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: "Reuters" STOCK: 01/02/2017 DATE: 49.0803
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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 0.2272727272727273 and the TextBlob polarity score is @Reuters.
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Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends.
|
STOCK: 01/02/2017 1_DAY_RETURN: 0.0035900351057349 2_DAY_RETURN: 0.009425370260573 3_DAY_RETURN: 0.0177280904965943 7_DAY_RETURN: 693341.0
|
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: 01/02/2017 LAST_PRICE: 0.0062835801737152 PX_VOLUME: 10.369000000000002 VOLATILITY_10D: 9.245 VOLATILITY_30D: -1.0 LSTM_POLARITY: 0.2272727272727273 TEXTBLOB_POLARITY: @Reuters
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Predicted 1_DAY_RETURN: 0.0035900351057349
Predicted 2_DAY_RETURN: 0.009425370260573
Predicted 7_DAY_RETURN: 693341.0
|
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
|
TWEET: "RT @trvo512: are you fucking kidding me? @McDonalds https://t.co/YgNCoLqJhr" 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
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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: "McDonald's" STOCK: 01/02/2017 DATE: 122.42
|
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 -0.6 and the TextBlob polarity score is @McDonalds.
|
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends.
|
STOCK: 01/02/2017 1_DAY_RETURN: 0.004901159941186 2_DAY_RETURN: 0.0035941839568697 3_DAY_RETURN: -0.0051462179382453 7_DAY_RETURN: 3233576.0
|
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: 01/02/2017 LAST_PRICE: 0.0012252899852964 PX_VOLUME: 7.607 VOLATILITY_10D: 9.573 VOLATILITY_30D: -1.0 LSTM_POLARITY: -0.6 TEXTBLOB_POLARITY: @McDonalds
|
Predicted 1_DAY_RETURN: 0.004901159941186
Predicted 2_DAY_RETURN: 0.0035941839568697
Predicted 7_DAY_RETURN: 3233576.0
|
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
|
TWEET: "@knowledge_owl I can't join in the lovefest #custserv @ZenYinger @Walmart" 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: "Walmart" STOCK: 01/02/2017 DATE: 66.23
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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 @Walmart.
|
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends.
|
STOCK: 01/02/2017 1_DAY_RETURN: 0.0028687905782877 2_DAY_RETURN: -0.0086063717348634 3_DAY_RETURN: 0.0099652725351048 7_DAY_RETURN: 9049972.0
|
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: 01/02/2017 LAST_PRICE: 0.0077004378680354 PX_VOLUME: 15.316 VOLATILITY_10D: 14.526 VOLATILITY_30D: -1.0 LSTM_POLARITY: 0.0 TEXTBLOB_POLARITY: @Walmart
|
Predicted 1_DAY_RETURN: 0.0028687905782877
Predicted 2_DAY_RETURN: -0.0086063717348634
Predicted 7_DAY_RETURN: 9049972.0
|
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
|
TWEET: "Dear @Apple @NintendoAmerica @Sony @samsung @LGUS @ATT & Other #CellPhone #VideoGames stop making so many #devices #update old end waste" 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: 01/02/2017 DATE: 128.75
|
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 0.06874999999999999 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: 01/02/2017 1_DAY_RETURN: -0.0553009708737864 2_DAY_RETURN: -0.0528155339805825 3_DAY_RETURN: -0.0533592233009709 7_DAY_RETURN: 111985040.0
|
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: 01/02/2017 LAST_PRICE: -0.0574757281553398 PX_VOLUME: 32.204 VOLATILITY_10D: 19.196 VOLATILITY_30D: -1.0 LSTM_POLARITY: 0.06874999999999999 TEXTBLOB_POLARITY: @Apple
|
Predicted 1_DAY_RETURN: -0.0553009708737864
Predicted 2_DAY_RETURN: -0.0528155339805825
Predicted 7_DAY_RETURN: 111985040.0
|
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
|
TWEET: "RT @SandyAvila37: @netflix Pls make #BATB yours and continue VinCat´s magical journey #NetflixSaveBatB https://t.co/aKmjtg3mui" 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: "Netflix" STOCK: 01/02/2017 DATE: 140.78
|
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: 01/02/2017 1_DAY_RETURN: 0.0031254439551072 2_DAY_RETURN: 0.0118624804659751 3_DAY_RETURN: -0.0089501349623525 7_DAY_RETURN: 6033422.0
|
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: 01/02/2017 LAST_PRICE: -0.0004972297201306 PX_VOLUME: 19.491 VOLATILITY_10D: 24.09800000000001 VOLATILITY_30D: 1.0 LSTM_POLARITY: 0.5 TEXTBLOB_POLARITY: @netflix
|
Predicted 1_DAY_RETURN: 0.0031254439551072
Predicted 2_DAY_RETURN: 0.0118624804659751
Predicted 7_DAY_RETURN: 6033422.0
|
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 ABBY Pottery Barn Kids Plush Cloth Baby Doll PBK Pink Hat North American Bear https://t.co/GyRR3hNvUM 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: "eBay" STOCK: 01/02/2017 DATE: 32.18
|
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 -0.05 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: 01/02/2017 1_DAY_RETURN: -0.000310752019888 2_DAY_RETURN: 0.0102548166563082 3_DAY_RETURN: -0.0605966438781851 7_DAY_RETURN: 9700776.0
|
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: 01/02/2017 LAST_PRICE: -0.0108763206960845 PX_VOLUME: 33.037 VOLATILITY_10D: 22.838 VOLATILITY_30D: -1.0 LSTM_POLARITY: -0.05 TEXTBLOB_POLARITY: @eBay
|
Predicted 1_DAY_RETURN: -0.000310752019888
Predicted 2_DAY_RETURN: 0.0102548166563082
Predicted 7_DAY_RETURN: 9700776.0
|
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
|
TWEET: "@realDonaldTrump Challenges to #Trump's immigration orders spread to more U.S. states https://t.co/Q0ZV29Op7u via @Reuters" 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: "Reuters" STOCK: 01/02/2017 DATE: 49.0803
|
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: 01/02/2017 1_DAY_RETURN: 0.0035900351057349 2_DAY_RETURN: 0.009425370260573 3_DAY_RETURN: 0.0177280904965943 7_DAY_RETURN: 693341.0
|
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: 01/02/2017 LAST_PRICE: 0.0062835801737152 PX_VOLUME: 10.369000000000002 VOLATILITY_10D: 9.245 VOLATILITY_30D: 1.0 LSTM_POLARITY: 0.5 TEXTBLOB_POLARITY: @Reuters
|
Predicted 1_DAY_RETURN: 0.0035900351057349
Predicted 2_DAY_RETURN: 0.009425370260573
Predicted 7_DAY_RETURN: 693341.0
|
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 2016 Animal Adventure Easter Yellow Duck Plush #AnimalAdventure https://t.co/CqhJZWw0t9 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: "eBay" STOCK: 01/02/2017 DATE: 32.18
|
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: 01/02/2017 1_DAY_RETURN: -0.000310752019888 2_DAY_RETURN: 0.0102548166563082 3_DAY_RETURN: -0.0605966438781851 7_DAY_RETURN: 9700776.0
|
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: 01/02/2017 LAST_PRICE: -0.0108763206960845 PX_VOLUME: 33.037 VOLATILITY_10D: 22.838 VOLATILITY_30D: -1.0 LSTM_POLARITY: 0.0 TEXTBLOB_POLARITY: @eBay
|
Predicted 1_DAY_RETURN: -0.000310752019888
Predicted 2_DAY_RETURN: 0.0102548166563082
Predicted 7_DAY_RETURN: 9700776.0
|
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 defies Wall Street with strong revival in iPhone sales https://t.co/mrYlGjXlAE" 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: "Reuters" STOCK: 01/02/2017 DATE: 49.0803
|
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 0.4333333333333333 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: 01/02/2017 1_DAY_RETURN: 0.0035900351057349 2_DAY_RETURN: 0.009425370260573 3_DAY_RETURN: 0.0177280904965943 7_DAY_RETURN: 693341.0
|
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: 01/02/2017 LAST_PRICE: 0.0062835801737152 PX_VOLUME: 10.369000000000002 VOLATILITY_10D: 9.245 VOLATILITY_30D: 1.0 LSTM_POLARITY: 0.4333333333333333 TEXTBLOB_POLARITY: @Reuters
|
Predicted 1_DAY_RETURN: 0.0035900351057349
Predicted 2_DAY_RETURN: 0.009425370260573
Predicted 7_DAY_RETURN: 693341.0
|
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