moqarashad/tweet_dataset · Datasets at Hugging Face

instruction stringclasses 3 values	input stringlengths 33 307	output stringlengths 66 248
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.	TWEET: "RT @robertoglezcano: @amazon #Patents Show Flying Warehouses That Send Delivery Drones To Your Door https://t.co/cZwL9QLhoC by… " 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: "Amazon" STOCK: 31/01/2017 DATE: 823.48	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 @amazon.
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: 0.0149244668965851 2_DAY_RETURN: 0.0149244668965851 3_DAY_RETURN: -0.0012629329188322 7_DAY_RETURN: 3137196.0	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: 0.0083790741730217 PX_VOLUME: 13.447 VOLATILITY_10D: 16.992 VOLATILITY_30D: 1.0 LSTM_POLARITY: 0.0 TEXTBLOB_POLARITY: @amazon	Predicted 1_DAY_RETURN: 0.0149244668965851 Predicted 2_DAY_RETURN: 0.0149244668965851 Predicted 7_DAY_RETURN: 3137196.0
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.	TWEET: "@FAME95FM1 Jamaicans make money with @Payoneer @PayPal, @paxuminc, @ecoPayz and @okpaycom https://t.co/FWzqUrgsqs " STOCK: PayPal 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: PayPal 1_DAY_RETURN: 0.0020110608345902 2_DAY_RETURN: 0.0123177476118653 3_DAY_RETURN: 0.0123177476118653 7_DAY_RETURN: 0.0548014077425842	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: PayPal LAST_PRICE: 39.78 PX_VOLUME: 9100057.0 VOLATILITY_10D: 18.769 VOLATILITY_30D: 16.099 LSTM_POLARITY: -1 TEXTBLOB_POLARITY: 0.0	Predicted 1_DAY_RETURN: 0.0020110608345902 Predicted 2_DAY_RETURN: 0.0123177476118653 Predicted 7_DAY_RETURN: 0.0548014077425842
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.	TWEET: "@CBSi Jamaicans make money with @Payoneer @PayPal, @paxuminc, @ecoPayz and @okpaycom https://t.co/FWzqUqYRyU " STOCK: PayPal 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: PayPal 1_DAY_RETURN: 0.0020110608345902 2_DAY_RETURN: 0.0123177476118653 3_DAY_RETURN: 0.0123177476118653 7_DAY_RETURN: 0.0548014077425842	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: PayPal LAST_PRICE: 39.78 PX_VOLUME: 9100057.0 VOLATILITY_10D: 18.769 VOLATILITY_30D: 16.099 LSTM_POLARITY: 1 TEXTBLOB_POLARITY: 0.0	Predicted 1_DAY_RETURN: 0.0020110608345902 Predicted 2_DAY_RETURN: 0.0123177476118653 Predicted 7_DAY_RETURN: 0.0548014077425842
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.	TWEET: "@Hitz92fm Jamaicans make money with @Payoneer @PayPal, @paxuminc, @ecoPayz and @okpaycom https://t.co/FWzqUqYRyU " STOCK: PayPal 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: PayPal 1_DAY_RETURN: 0.0020110608345902 2_DAY_RETURN: 0.0123177476118653 3_DAY_RETURN: 0.0123177476118653 7_DAY_RETURN: 0.0548014077425842	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: PayPal LAST_PRICE: 39.78 PX_VOLUME: 9100057.0 VOLATILITY_10D: 18.769 VOLATILITY_30D: 16.099 LSTM_POLARITY: -1 TEXTBLOB_POLARITY: 0.0	Predicted 1_DAY_RETURN: 0.0020110608345902 Predicted 2_DAY_RETURN: 0.0123177476118653 Predicted 7_DAY_RETURN: 0.0548014077425842
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.	TWEET: "RT @loadsofvans: Retweet this post & follow @loadsofvans for a chance to win a £100 @amazon voucher #win #giveaway #competition… " 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: "Amazon" STOCK: 31/01/2017 DATE: 823.48	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 0.8 and the TextBlob polarity score is @amazon.
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: 0.0149244668965851 2_DAY_RETURN: 0.0149244668965851 3_DAY_RETURN: -0.0012629329188322 7_DAY_RETURN: 3137196.0	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: 0.0083790741730217 PX_VOLUME: 13.447 VOLATILITY_10D: 16.992 VOLATILITY_30D: -1.0 LSTM_POLARITY: 0.8 TEXTBLOB_POLARITY: @amazon	Predicted 1_DAY_RETURN: 0.0149244668965851 Predicted 2_DAY_RETURN: 0.0149244668965851 Predicted 7_DAY_RETURN: 3137196.0
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.	TWEET: "RT @Disney: See The Newest Trailer for #BeautyAndTheBeast: https://t.co/JfINQL6EbV https://t.co/jZjNR0GRk9" STOCK: nan DATE: nan	Sentiment: (Provide sentiment here) Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is nan and the TextBlob polarity score is nan.
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends.	STOCK: nan 1_DAY_RETURN: nan 2_DAY_RETURN: nan 3_DAY_RETURN: nan 7_DAY_RETURN: nan	The stock shows a neutral return trend over the specified periods.
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN.	STOCK: nan LAST_PRICE: nan PX_VOLUME: nan VOLATILITY_10D: nan VOLATILITY_30D: nan LSTM_POLARITY: nan TEXTBLOB_POLARITY: nan	Predicted 1_DAY_RETURN: nan Predicted 2_DAY_RETURN: nan Predicted 7_DAY_RETURN: nan
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.	TWEET: "Disney" STOCK: 31/01/2017 DATE: 110.65	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 @Disney.
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends.	STOCK: 31/01/2017 1_DAY_RETURN: -0.0122006326253954 2_DAY_RETURN: -0.0122006326253954 3_DAY_RETURN: -0.0248531405332128 7_DAY_RETURN: 8485838.0	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: 0.0026208766380478 PX_VOLUME: 12.229 VOLATILITY_10D: 12.982 VOLATILITY_30D: 1.0 LSTM_POLARITY: 0.0 TEXTBLOB_POLARITY: @Disney	Predicted 1_DAY_RETURN: -0.0122006326253954 Predicted 2_DAY_RETURN: -0.0122006326253954 Predicted 7_DAY_RETURN: 8485838.0
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.	TWEET: "RT @tarastrong: Just got @facebook back! I REALLY want to thank you all for your support. Being able to speak out against bullies is SO IMP…" STOCK: nan DATE: nan	Sentiment: (Provide sentiment here) Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is nan and the TextBlob polarity score is nan.
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends.	STOCK: nan 1_DAY_RETURN: nan 2_DAY_RETURN: nan 3_DAY_RETURN: nan 7_DAY_RETURN: nan	The stock shows a neutral return trend over the specified periods.
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN.	STOCK: nan LAST_PRICE: nan PX_VOLUME: nan VOLATILITY_10D: nan VOLATILITY_30D: nan LSTM_POLARITY: nan TEXTBLOB_POLARITY: nan	Predicted 1_DAY_RETURN: nan Predicted 2_DAY_RETURN: nan Predicted 7_DAY_RETURN: nan
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.	TWEET: "Facebook" STOCK: 31/01/2017 DATE: 130.32	Sentiment: (Provide sentiment here) Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is 0.0 and the TextBlob polarity score is @facebook.
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends.	STOCK: 31/01/2017 1_DAY_RETURN: 0.0142725598526704 2_DAY_RETURN: 0.0142725598526704 3_DAY_RETURN: -0.0072897483118476 7_DAY_RETURN: 19790484.0	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: 0.0050644567219152 PX_VOLUME: 15.121 VOLATILITY_10D: 16.219 VOLATILITY_30D: 1.0 LSTM_POLARITY: 0.0 TEXTBLOB_POLARITY: @facebook	Predicted 1_DAY_RETURN: 0.0142725598526704 Predicted 2_DAY_RETURN: 0.0142725598526704 Predicted 7_DAY_RETURN: 19790484.0
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.	TWEET: "RT @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: "RT @DrJimmyStar: Check out Mens Encore by Fiesso Black Leather Driving Shoes Size 12 https://t.co/QzMHHh7Cbv @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 -0.16666666666666666 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: -0.16666666666666666 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 Betsey Johnson Heart Tab Red Dual Handle Two Tassel Purse NWT https://t.co/11nAogJeiY @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: "@Reuters lol anyone who challenges him gets fired. Soon it will be jail or they'll go missing- maybe to a gulag" 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 0.8 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.
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN.	STOCK: 31/01/2017 LAST_PRICE: -0.0026767256477695 PX_VOLUME: 9.665 VOLATILITY_10D: 9.094 VOLATILITY_30D: -1.0 LSTM_POLARITY: 0.8 TEXTBLOB_POLARITY: @Reuters	Predicted 1_DAY_RETURN: 0.0031221716708478 Predicted 2_DAY_RETURN: 0.0031221716708478 Predicted 7_DAY_RETURN: 547029.0
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.	TWEET: "RT @jhill1105: @loadsofvans Retweet this post & follow @loadsofvans for a chance to win a £100 @amazon voucher #win #giveaway #competition" 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: "Amazon" STOCK: 31/01/2017 DATE: 823.48	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 0.8 and the TextBlob polarity score is @amazon.
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: 0.0149244668965851 2_DAY_RETURN: 0.0149244668965851 3_DAY_RETURN: -0.0012629329188322 7_DAY_RETURN: 3137196.0	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: 0.0083790741730217 PX_VOLUME: 13.447 VOLATILITY_10D: 16.992 VOLATILITY_30D: -1.0 LSTM_POLARITY: 0.8 TEXTBLOB_POLARITY: @amazon	Predicted 1_DAY_RETURN: 0.0149244668965851 Predicted 2_DAY_RETURN: 0.0149244668965851 Predicted 7_DAY_RETURN: 3137196.0
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.	TWEET: "@gawker Jamaicans make money with @Payoneer @PayPal, @paxuminc, @ecoPayz and @okpaycom https://t.co/FWzqUrgsqs " STOCK: PayPal 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: PayPal 1_DAY_RETURN: 0.0020110608345902 2_DAY_RETURN: 0.0123177476118653 3_DAY_RETURN: 0.0123177476118653 7_DAY_RETURN: 0.0548014077425842	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: PayPal LAST_PRICE: 39.78 PX_VOLUME: 9100057.0 VOLATILITY_10D: 18.769 VOLATILITY_30D: 16.099 LSTM_POLARITY: -1 TEXTBLOB_POLARITY: 0.0	Predicted 1_DAY_RETURN: 0.0020110608345902 Predicted 2_DAY_RETURN: 0.0123177476118653 Predicted 7_DAY_RETURN: 0.0548014077425842
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.	TWEET: "Congrats to our #CNWF alumni @helenshang! Don't miss her episode of #h50 on @CBS this Friday! #AAPIwriters… https://t.co/G3OrzrtK7W" STOCK: nan DATE: nan	Sentiment: (Provide sentiment here) Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is nan and the TextBlob polarity score is nan.
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends.	STOCK: nan 1_DAY_RETURN: nan 2_DAY_RETURN: nan 3_DAY_RETURN: nan 7_DAY_RETURN: nan	The stock shows a neutral return trend over the specified periods.
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN.	STOCK: nan LAST_PRICE: nan PX_VOLUME: nan VOLATILITY_10D: nan VOLATILITY_30D: nan LSTM_POLARITY: nan TEXTBLOB_POLARITY: nan	Predicted 1_DAY_RETURN: nan Predicted 2_DAY_RETURN: nan Predicted 7_DAY_RETURN: nan
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.	TWEET: "CBS" STOCK: 31/01/2017 DATE: 64.49	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 @CBS.
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: 0.0038765700108543 2_DAY_RETURN: 0.0038765700108543 3_DAY_RETURN: -0.0234144828655605 7_DAY_RETURN: 3523867.0	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: -0.0013955652039074 PX_VOLUME: 16.338 VOLATILITY_10D: 16.302 VOLATILITY_30D: 1.0 LSTM_POLARITY: 0.0 TEXTBLOB_POLARITY: @CBS	Predicted 1_DAY_RETURN: 0.0038765700108543 Predicted 2_DAY_RETURN: 0.0038765700108543 Predicted 7_DAY_RETURN: 3523867.0
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.	TWEET: "RT @cultcommoncore: Dumping @Starbucks Is like dropping cable tv. You wonder how you paid SO much For such crap For SO long.… " 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 0.0.
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends.	STOCK: 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: 0.0	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: "RT @DrJimmyStar: Check out Mens Encore by Fiesso Black Leather Driving Shoes Size 12 https://t.co/QzMHHh7Cbv @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 -0.16666666666666666 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: -0.16666666666666666 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: "@amazon has your back when it comes to food shopping with tech replacing queues, just grab and go https://t.co/D83kIqFMAK " 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 @tarastrong: Just got @facebook back! I REALLY want to thank you all for your support. Being able to speak out against bullies is SO IMP…" STOCK: nan DATE: nan	Sentiment: (Provide sentiment here) Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is nan and the TextBlob polarity score is nan.
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends.	STOCK: nan 1_DAY_RETURN: nan 2_DAY_RETURN: nan 3_DAY_RETURN: nan 7_DAY_RETURN: nan	The stock shows a neutral return trend over the specified periods.
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN.	STOCK: nan LAST_PRICE: nan PX_VOLUME: nan VOLATILITY_10D: nan VOLATILITY_30D: nan LSTM_POLARITY: nan TEXTBLOB_POLARITY: nan	Predicted 1_DAY_RETURN: nan Predicted 2_DAY_RETURN: nan Predicted 7_DAY_RETURN: nan
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.	TWEET: "Facebook" STOCK: 31/01/2017 DATE: 130.32	Sentiment: (Provide sentiment here) Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is 0.0 and the TextBlob polarity score is @facebook.
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends.	STOCK: 31/01/2017 1_DAY_RETURN: 0.0142725598526704 2_DAY_RETURN: 0.0142725598526704 3_DAY_RETURN: -0.0072897483118476 7_DAY_RETURN: 19790484.0	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: 0.0050644567219152 PX_VOLUME: 15.121 VOLATILITY_10D: 16.219 VOLATILITY_30D: 1.0 LSTM_POLARITY: 0.0 TEXTBLOB_POLARITY: @facebook	Predicted 1_DAY_RETURN: 0.0142725598526704 Predicted 2_DAY_RETURN: 0.0142725598526704 Predicted 7_DAY_RETURN: 19790484.0
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.	TWEET: "RT @prosper_p_r: The incredible @VanessaKirby at the #sagawards2017! #thecrown @netflix https://t.co/AR3qhBdILX" 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: 31/01/2017 DATE: 140.71	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 1.0 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: 31/01/2017 1_DAY_RETURN: 0.0123658588586453 2_DAY_RETURN: 0.0123658588586453 3_DAY_RETURN: -0.0042640892616018 7_DAY_RETURN: 4411631.0	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: 0.0036244758723615 PX_VOLUME: 27.398000000000003 VOLATILITY_10D: 24.135 VOLATILITY_30D: 1.0 LSTM_POLARITY: 1.0 TEXTBLOB_POLARITY: @netflix	Predicted 1_DAY_RETURN: 0.0123658588586453 Predicted 2_DAY_RETURN: 0.0123658588586453 Predicted 7_DAY_RETURN: 4411631.0
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.	TWEET: "@AppleSupport @Apple omg! Innovation alert! Guess what! #change if Siri could reverse engineer dialogue depending our directional input via" 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 0.0 and the TextBlob polarity score is @Apple.
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends.	STOCK: 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: 0.0 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: "Check out If Love Is Blind Why is Lingerie Popular Ceramic Coffee Mugs \| Ceramic Mugs https://t.co/5pSUELBCje 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.

Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.

TWEET: "RT @robertoglezcano: @amazon #Patents Show Flying Warehouses That Send Delivery Drones To Your Door https://t.co/cZwL9QLhoC by… " 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: "Amazon" STOCK: 31/01/2017 DATE: 823.48

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 @amazon.

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: 0.0149244668965851 2_DAY_RETURN: 0.0149244668965851 3_DAY_RETURN: -0.0012629329188322 7_DAY_RETURN: 3137196.0

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: 0.0083790741730217 PX_VOLUME: 13.447 VOLATILITY_10D: 16.992 VOLATILITY_30D: 1.0 LSTM_POLARITY: 0.0 TEXTBLOB_POLARITY: @amazon

Predicted 1_DAY_RETURN: 0.0149244668965851 Predicted 2_DAY_RETURN: 0.0149244668965851 Predicted 7_DAY_RETURN: 3137196.0

Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.

TWEET: "@FAME95FM1 Jamaicans make money with @Payoneer @PayPal, @paxuminc, @ecoPayz and @okpaycom https://t.co/FWzqUrgsqs " STOCK: PayPal 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: PayPal 1_DAY_RETURN: 0.0020110608345902 2_DAY_RETURN: 0.0123177476118653 3_DAY_RETURN: 0.0123177476118653 7_DAY_RETURN: 0.0548014077425842

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: PayPal LAST_PRICE: 39.78 PX_VOLUME: 9100057.0 VOLATILITY_10D: 18.769 VOLATILITY_30D: 16.099 LSTM_POLARITY: -1 TEXTBLOB_POLARITY: 0.0

Predicted 1_DAY_RETURN: 0.0020110608345902 Predicted 2_DAY_RETURN: 0.0123177476118653 Predicted 7_DAY_RETURN: 0.0548014077425842

Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.

TWEET: "@CBSi Jamaicans make money with @Payoneer @PayPal, @paxuminc, @ecoPayz and @okpaycom https://t.co/FWzqUqYRyU " STOCK: PayPal 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: PayPal 1_DAY_RETURN: 0.0020110608345902 2_DAY_RETURN: 0.0123177476118653 3_DAY_RETURN: 0.0123177476118653 7_DAY_RETURN: 0.0548014077425842

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: PayPal LAST_PRICE: 39.78 PX_VOLUME: 9100057.0 VOLATILITY_10D: 18.769 VOLATILITY_30D: 16.099 LSTM_POLARITY: 1 TEXTBLOB_POLARITY: 0.0

Predicted 1_DAY_RETURN: 0.0020110608345902 Predicted 2_DAY_RETURN: 0.0123177476118653 Predicted 7_DAY_RETURN: 0.0548014077425842

Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.

TWEET: "@Hitz92fm Jamaicans make money with @Payoneer @PayPal, @paxuminc, @ecoPayz and @okpaycom https://t.co/FWzqUqYRyU " STOCK: PayPal DATE: 31/01/2017