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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: 31/01/2017 1_DAY_RETURN: -0.0028907303912121 2_DAY_RETURN: -0.0028907303912121 3_DAY_RETURN: 0.0089612642127576 7_DAY_RETURN: 3846647.0
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: 31/01/2017 LAST_PRICE: -0.0008672191173636 PX_VOLUME: 8.876 VOLATILITY_10D: 8.55 VOLATILITY_30D: -1.0 LSTM_POLARITY: 0.0 TEXTBLOB_POLARITY: @pepsi
Predicted 1_DAY_RETURN: -0.0028907303912121 Predicted 2_DAY_RETURN: -0.0028907303912121 Predicted 7_DAY_RETURN: 3846647.0
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "RT @DrJimmyStar: Check out Westland Giftware Ceramic Spiderman Cookie Jar NIB Multi-Color https://t.co/IB65e9yvXS @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: 31/01/2017 DATE: 31.83
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: 31/01/2017 1_DAY_RETURN: 0.021363493559535 2_DAY_RETURN: 0.021363493559535 3_DAY_RETURN: -0.0578071002199183 7_DAY_RETURN: 9469076.0
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: 31/01/2017 LAST_PRICE: 0.0106817467797676 PX_VOLUME: 33.029 VOLATILITY_10D: 22.932 VOLATILITY_30D: -1.0 LSTM_POLARITY: 0.0 TEXTBLOB_POLARITY: @eBay
Predicted 1_DAY_RETURN: 0.021363493559535 Predicted 2_DAY_RETURN: 0.021363493559535 Predicted 7_DAY_RETURN: 9469076.0
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "@usa_wk Thanks for following me! Be sure to ✔ out my book "The Twins of Aerobith" available @amazon / @BNBuzz /… https://t.co/hQh72wBfy5 " STOCK: Amazon DATE: 31/01/2017
Sentiment: (Provide sentiment here) Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is 1 and the TextBlob polarity score is 0.1.
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends.
STOCK: Amazon 1_DAY_RETURN: 0.0083790741730217 2_DAY_RETURN: 0.0149244668965851 3_DAY_RETURN: 0.0149244668965851 7_DAY_RETURN: -0.0012629329188322
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: 823.48 PX_VOLUME: 3137196.0 VOLATILITY_10D: 13.447 VOLATILITY_30D: 16.992 LSTM_POLARITY: 1 TEXTBLOB_POLARITY: 0.1
Predicted 1_DAY_RETURN: 0.0083790741730217 Predicted 2_DAY_RETURN: 0.0149244668965851 Predicted 7_DAY_RETURN: -0.0012629329188322
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "RT @nikitakhara: Thank you, @Starbucks CEO for committing to hire 10,000 refugees. To all those tweeting #boycottstarbucks, thanks for the… " STOCK: Starbucks DATE: 31/01/2017
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 0.2.
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends.
STOCK: Starbucks 1_DAY_RETURN: 0.0123143788482433 2_DAY_RETURN: 0.0162984425932632 3_DAY_RETURN: 0.0162984425932632 7_DAY_RETURN: 0.0583122057225642
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: Starbucks LAST_PRICE: 55.22 PX_VOLUME: 14307985.0 VOLATILITY_10D: 23.916 VOLATILITY_30D: 17.298 LSTM_POLARITY: 1 TEXTBLOB_POLARITY: 0.2
Predicted 1_DAY_RETURN: 0.0123143788482433 Predicted 2_DAY_RETURN: 0.0162984425932632 Predicted 7_DAY_RETURN: 0.0583122057225642
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "@amazon needs to keep it going and ban all Trump merchandise off its Store, official and non-offical. that goes for all his family members. " STOCK: Amazon DATE: 31/01/2017
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: 0.0083790741730217 2_DAY_RETURN: 0.0149244668965851 3_DAY_RETURN: 0.0149244668965851 7_DAY_RETURN: -0.0012629329188322
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: 823.48 PX_VOLUME: 3137196.0 VOLATILITY_10D: 13.447 VOLATILITY_30D: 16.992 LSTM_POLARITY: 1 TEXTBLOB_POLARITY: 0.0
Predicted 1_DAY_RETURN: 0.0083790741730217 Predicted 2_DAY_RETURN: 0.0149244668965851 Predicted 7_DAY_RETURN: -0.0012629329188322
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "RT @SherriHill: Just got this from the warehouse where they filled an entire @UPS trailer. New dresses will be in your local store… " 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: "UPS" STOCK: 31/01/2017 DATE: 109.13
Sentiment: (Provide sentiment here) Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is 0.2 and the TextBlob polarity score is @UPS.
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends.
STOCK: 31/01/2017 1_DAY_RETURN: 0.0821039127645927 2_DAY_RETURN: 0.0821039127645927 3_DAY_RETURN: 0.0634106111976541 7_DAY_RETURN: 12755479.0
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: 31/01/2017 LAST_PRICE: 0.0723907266562815 PX_VOLUME: 40.751 VOLATILITY_10D: 23.277 VOLATILITY_30D: -1.0 LSTM_POLARITY: 0.2 TEXTBLOB_POLARITY: @UPS
Predicted 1_DAY_RETURN: 0.0821039127645927 Predicted 2_DAY_RETURN: 0.0821039127645927 Predicted 7_DAY_RETURN: 12755479.0
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "RT @BigWangTheoryy: Uber CEO came out like: YOU going broke! & YOU going broke!! We all going BROKE!! @TeslaMotors @GM @pepsi @IBM… " STOCK: Pepsi DATE: 31/01/2017
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: Pepsi 1_DAY_RETURN: -0.0008672191173636 2_DAY_RETURN: -0.0028907303912121 3_DAY_RETURN: -0.0028907303912121 7_DAY_RETURN: 0.0089612642127576
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: Pepsi LAST_PRICE: 103.78 PX_VOLUME: 3846647.0 VOLATILITY_10D: 8.876 VOLATILITY_30D: 8.55 LSTM_POLARITY: 1 TEXTBLOB_POLARITY: 0.0
Predicted 1_DAY_RETURN: -0.0008672191173636 Predicted 2_DAY_RETURN: -0.0028907303912121 Predicted 7_DAY_RETURN: 0.0089612642127576
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
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: 31/01/2017 DATE: 122.57
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: 31/01/2017 1_DAY_RETURN: 0.002365994941666 2_DAY_RETURN: 0.002365994941666 3_DAY_RETURN: -0.0124010769356285 7_DAY_RETURN: 3733323.0
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: 31/01/2017 LAST_PRICE: 0.0036713714612058 PX_VOLUME: 7.972 VOLATILITY_10D: 9.585 VOLATILITY_30D: -1.0 LSTM_POLARITY: -0.6 TEXTBLOB_POLARITY: @McDonalds
Predicted 1_DAY_RETURN: 0.002365994941666 Predicted 2_DAY_RETURN: 0.002365994941666 Predicted 7_DAY_RETURN: 3733323.0
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "Will be the END of @Starbucks ... finally! 💥🇺🇸💥@Charlie4Trump @Olivemykids3 @DebAlwaystrump" 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: 31/01/2017 DATE: 55.22
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: 31/01/2017 1_DAY_RETURN: 0.0162984425932632 2_DAY_RETURN: 0.0162984425932632 3_DAY_RETURN: 0.0583122057225642 7_DAY_RETURN: 14307985.0
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: 31/01/2017 LAST_PRICE: 0.0123143788482433 PX_VOLUME: 23.916 VOLATILITY_10D: 17.298 VOLATILITY_30D: -1.0 LSTM_POLARITY: 0.0 TEXTBLOB_POLARITY: @Starbucks
Predicted 1_DAY_RETURN: 0.0162984425932632 Predicted 2_DAY_RETURN: 0.0162984425932632 Predicted 7_DAY_RETURN: 14307985.0
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "RT @wccn: @kindNotWeak2017 @carlbernstein @Reuters @Khanoisseur Treason is worthy of an investigation, no matter how hard. " STOCK: Reuters DATE: 31/01/2017
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 0.020833333333333315.
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: -0.0026767256477695 2_DAY_RETURN: 0.0031221716708478 3_DAY_RETURN: 0.0031221716708478 7_DAY_RETURN: 0.0044605344947324
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: Reuters LAST_PRICE: 49.3887 PX_VOLUME: 547029.0 VOLATILITY_10D: 9.665 VOLATILITY_30D: 9.094 LSTM_POLARITY: 1 TEXTBLOB_POLARITY: 0.020833333333333315
Predicted 1_DAY_RETURN: -0.0026767256477695 Predicted 2_DAY_RETURN: 0.0031221716708478 Predicted 7_DAY_RETURN: 0.0044605344947324
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "RT @DrJimmyStar: Check out Betsey Johnson Cosmetic Bag NWT https://t.co/3WNMpr9Wzw @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: 31/01/2017 DATE: 31.83
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: 31/01/2017 1_DAY_RETURN: 0.021363493559535 2_DAY_RETURN: 0.021363493559535 3_DAY_RETURN: -0.0578071002199183 7_DAY_RETURN: 9469076.0
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: 31/01/2017 LAST_PRICE: 0.0106817467797676 PX_VOLUME: 33.029 VOLATILITY_10D: 22.932 VOLATILITY_30D: -1.0 LSTM_POLARITY: 0.0 TEXTBLOB_POLARITY: @eBay
Predicted 1_DAY_RETURN: 0.021363493559535 Predicted 2_DAY_RETURN: 0.021363493559535 Predicted 7_DAY_RETURN: 9469076.0
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "RT @DrJimmyStar: Check out Westland Giftware Ceramic Spiderman Cookie Jar NIB Multi-Color https://t.co/IB65e9yvXS @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: 31/01/2017 DATE: 31.83
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: 31/01/2017 1_DAY_RETURN: 0.021363493559535 2_DAY_RETURN: 0.021363493559535 3_DAY_RETURN: -0.0578071002199183 7_DAY_RETURN: 9469076.0
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: 31/01/2017 LAST_PRICE: 0.0106817467797676 PX_VOLUME: 33.029 VOLATILITY_10D: 22.932 VOLATILITY_30D: -1.0 LSTM_POLARITY: 0.0 TEXTBLOB_POLARITY: @eBay
Predicted 1_DAY_RETURN: 0.021363493559535 Predicted 2_DAY_RETURN: 0.021363493559535 Predicted 7_DAY_RETURN: 9469076.0
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
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: 31/01/2017 DATE: 122.57
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: 31/01/2017 1_DAY_RETURN: 0.002365994941666 2_DAY_RETURN: 0.002365994941666 3_DAY_RETURN: -0.0124010769356285 7_DAY_RETURN: 3733323.0
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: 31/01/2017 LAST_PRICE: 0.0036713714612058 PX_VOLUME: 7.972 VOLATILITY_10D: 9.585 VOLATILITY_30D: -1.0 LSTM_POLARITY: -0.6 TEXTBLOB_POLARITY: @McDonalds
Predicted 1_DAY_RETURN: 0.002365994941666 Predicted 2_DAY_RETURN: 0.002365994941666 Predicted 7_DAY_RETURN: 3733323.0
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "RT @amazon: #AmazonPrime boxes, the key to a cat's heart. #PrimePet https://t.co/jwQzTIHBBT " STOCK: Amazon DATE: 31/01/2017
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: 0.0083790741730217 2_DAY_RETURN: 0.0149244668965851 3_DAY_RETURN: 0.0149244668965851 7_DAY_RETURN: -0.0012629329188322
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: 823.48 PX_VOLUME: 3137196.0 VOLATILITY_10D: 13.447 VOLATILITY_30D: 16.992 LSTM_POLARITY: -1 TEXTBLOB_POLARITY: 0.0
Predicted 1_DAY_RETURN: 0.0083790741730217 Predicted 2_DAY_RETURN: 0.0149244668965851 Predicted 7_DAY_RETURN: -0.0012629329188322
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "RT @DrJimmyStar: Check out gx by Gwen Stefani Hadda Purse NWT https://t.co/19wQIBu0UV @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: 31/01/2017 DATE: 31.83
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: 31/01/2017 1_DAY_RETURN: 0.021363493559535 2_DAY_RETURN: 0.021363493559535 3_DAY_RETURN: -0.0578071002199183 7_DAY_RETURN: 9469076.0
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: 31/01/2017 LAST_PRICE: 0.0106817467797676 PX_VOLUME: 33.029 VOLATILITY_10D: 22.932 VOLATILITY_30D: 1.0 LSTM_POLARITY: 0.0 TEXTBLOB_POLARITY: @eBay
Predicted 1_DAY_RETURN: 0.021363493559535 Predicted 2_DAY_RETURN: 0.021363493559535 Predicted 7_DAY_RETURN: 9469076.0
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "RT @DrJimmyStar: Check out g By Marciano Ladies Animal Print Purse NWT https://t.co/9g9PqwAdex @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: 31/01/2017 DATE: 31.83
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: 31/01/2017 1_DAY_RETURN: 0.021363493559535 2_DAY_RETURN: 0.021363493559535 3_DAY_RETURN: -0.0578071002199183 7_DAY_RETURN: 9469076.0
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: 31/01/2017 LAST_PRICE: 0.0106817467797676 PX_VOLUME: 33.029 VOLATILITY_10D: 22.932 VOLATILITY_30D: -1.0 LSTM_POLARITY: 0.0 TEXTBLOB_POLARITY: @eBay
Predicted 1_DAY_RETURN: 0.021363493559535 Predicted 2_DAY_RETURN: 0.021363493559535 Predicted 7_DAY_RETURN: 9469076.0
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "@Apple #1 in smartphones for first time since Q414 via @Canalys https://t.co/SpSgNT6z4w" 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: 31/01/2017 DATE: 121.35
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 @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: 31/01/2017 1_DAY_RETURN: 0.0049443757725587 2_DAY_RETURN: 0.0049443757725587 3_DAY_RETURN: -0.011372064276885 7_DAY_RETURN: 49200993.0
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: 31/01/2017 LAST_PRICE: 0.0023073753605274 PX_VOLUME: 9.114 VOLATILITY_10D: 8.417 VOLATILITY_30D: 1.0 LSTM_POLARITY: 0.25 TEXTBLOB_POLARITY: @Apple
Predicted 1_DAY_RETURN: 0.0049443757725587 Predicted 2_DAY_RETURN: 0.0049443757725587 Predicted 7_DAY_RETURN: 49200993.0
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "RT @Marshalled_It: @HP So I know this may be a bit belated, but my 3 year old laptop, which never really left my desk, just refuses to… " STOCK: HP DATE: 31/01/2017
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 0.05.
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends.
STOCK: HP 1_DAY_RETURN: -0.013953488372093 2_DAY_RETURN: -0.0166112956810631 3_DAY_RETURN: -0.0166112956810631 7_DAY_RETURN: 0.0079734219269102
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: HP LAST_PRICE: 15.05 PX_VOLUME: 14889607.0 VOLATILITY_10D: 27.84800000000001 VOLATILITY_30D: 19.862 LSTM_POLARITY: 1 TEXTBLOB_POLARITY: 0.05
Predicted 1_DAY_RETURN: -0.013953488372093 Predicted 2_DAY_RETURN: -0.0166112956810631 Predicted 7_DAY_RETURN: 0.0079734219269102
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "RT @greeneyes0084: 🚨Breaking: @Starbucks will be changing it's name to Refugeebucks to honor the people taking jobs from Americans..… " 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: 31/01/2017 DATE: 55.22
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: 31/01/2017 1_DAY_RETURN: 0.0162984425932632 2_DAY_RETURN: 0.0162984425932632 3_DAY_RETURN: 0.0583122057225642 7_DAY_RETURN: 14307985.0
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: 31/01/2017 LAST_PRICE: 0.0123143788482433 PX_VOLUME: 23.916 VOLATILITY_10D: 17.298 VOLATILITY_30D: 1.0 LSTM_POLARITY: 0.0 TEXTBLOB_POLARITY: @Starbucks
Predicted 1_DAY_RETURN: 0.0162984425932632 Predicted 2_DAY_RETURN: 0.0162984425932632 Predicted 7_DAY_RETURN: 14307985.0
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "RT @DrJimmyStar: Check out gx by Gwen Stefani Hadda Purse NWT https://t.co/19wQIBu0UV @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: 31/01/2017 DATE: 31.83
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: 31/01/2017 1_DAY_RETURN: 0.021363493559535 2_DAY_RETURN: 0.021363493559535 3_DAY_RETURN: -0.0578071002199183 7_DAY_RETURN: 9469076.0
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: 31/01/2017 LAST_PRICE: 0.0106817467797676 PX_VOLUME: 33.029 VOLATILITY_10D: 22.932 VOLATILITY_30D: 1.0 LSTM_POLARITY: 0.0 TEXTBLOB_POLARITY: @eBay
Predicted 1_DAY_RETURN: 0.021363493559535 Predicted 2_DAY_RETURN: 0.021363493559535 Predicted 7_DAY_RETURN: 9469076.0
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "@CornellWBrooks @Karnythia @NAACP @DemAwakening @AP @civilrightsorg @DemSpring @ADL_National @nytimes @Reuters arrested for what??!!!" 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: 31/01/2017 DATE: 49.3887
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: 31/01/2017 1_DAY_RETURN: 0.0031221716708478 2_DAY_RETURN: 0.0031221716708478 3_DAY_RETURN: 0.0044605344947324 7_DAY_RETURN: 547029.0
The stock shows a consistent positive return trend over the specified periods.