Datasets:
moqarashad
/
tweet_dataset

Dataset card Data Studio Files Community
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3 values
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Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "RT @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: 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: "@redheelsandbags @SouthLoneStar @DjangoWaistDown @Starbucks Thanks Chuck. Don't worry I may have been wrong but I don't Troll people" 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: 01/02/2017 DATE: 53.9
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 @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: 01/02/2017 1_DAY_RETURN: 0.0371057513914656 2_DAY_RETURN: 0.0411873840445268 3_DAY_RETURN: 0.0890538033395177 7_DAY_RETURN: 18796871.0
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: 0.0244897959183673 PX_VOLUME: 25.781 VOLATILITY_10D: 18.576 VOLATILITY_30D: 1.0 LSTM_POLARITY: 0.2 TEXTBLOB_POLARITY: @Starbucks
Predicted 1_DAY_RETURN: 0.0371057513914656 Predicted 2_DAY_RETURN: 0.0411873840445268 Predicted 7_DAY_RETURN: 18796871.0
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "@CBS @SuperiorDonuts glazed donuts #Tweet4Sweets" 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: 01/02/2017 DATE: 64.6
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: 01/02/2017 1_DAY_RETURN: -0.0030959752321979 2_DAY_RETURN: 0.0021671826625387 3_DAY_RETURN: -0.024767801857585 7_DAY_RETURN: 2123243.0
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: -0.0017027863777089 PX_VOLUME: 13.477 VOLATILITY_10D: 16.281 VOLATILITY_30D: -1.0 LSTM_POLARITY: 0.0 TEXTBLOB_POLARITY: @CBS
Predicted 1_DAY_RETURN: -0.0030959752321979 Predicted 2_DAY_RETURN: 0.0021671826625387 Predicted 7_DAY_RETURN: 2123243.0
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "@pasoquen @Starbucks um... well... I can't say I haven't used the bathroom. :-)" 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: 01/02/2017 DATE: 53.9
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: 01/02/2017 1_DAY_RETURN: 0.0371057513914656 2_DAY_RETURN: 0.0411873840445268 3_DAY_RETURN: 0.0890538033395177 7_DAY_RETURN: 18796871.0
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: 0.0244897959183673 PX_VOLUME: 25.781 VOLATILITY_10D: 18.576 VOLATILITY_30D: -1.0 LSTM_POLARITY: 0.0 TEXTBLOB_POLARITY: @Starbucks
Predicted 1_DAY_RETURN: 0.0371057513914656 Predicted 2_DAY_RETURN: 0.0411873840445268 Predicted 7_DAY_RETURN: 18796871.0
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "@deadmau5 I don't think that @Google listens to your music https://t.co/0GleoElOoB " STOCK: Google DATE: 01/02/2017
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: Google 1_DAY_RETURN: 0.0060718316078701 2_DAY_RETURN: 0.0105367744467887 3_DAY_RETURN: 0.0365413865855453 7_DAY_RETURN: 0.0530027967224376
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: Google LAST_PRICE: 815.24 PX_VOLUME: 2251047.0 VOLATILITY_10D: 21.579 VOLATILITY_30D: 15.049 LSTM_POLARITY: 1 TEXTBLOB_POLARITY: 0.0
Predicted 1_DAY_RETURN: 0.0060718316078701 Predicted 2_DAY_RETURN: 0.0105367744467887 Predicted 7_DAY_RETURN: 0.0530027967224376
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "FREE 4 a limited time - Secrets Of Manipulation: Forbidden Secrets They Don't Teach You About In Scho... https://t.co/IFirNjy2nH via @amazon" STOCK: nan DATE: nan
Sentiment: (Provide sentiment here) Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is nan and the TextBlob polarity score is nan.
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: 01/02/2017 DATE: 832.35
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 0.1642857142857143 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: 01/02/2017 1_DAY_RETURN: -0.0023667928155223 2_DAY_RETURN: 0.0041088484411605 3_DAY_RETURN: 0.0050099116958009 7_DAY_RETURN: 3850181.0
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: -0.0106565747582146 PX_VOLUME: 14.201 VOLATILITY_10D: 16.989 VOLATILITY_30D: 1.0 LSTM_POLARITY: 0.1642857142857143 TEXTBLOB_POLARITY: @amazon
Predicted 1_DAY_RETURN: -0.0023667928155223 Predicted 2_DAY_RETURN: 0.0041088484411605 Predicted 7_DAY_RETURN: 3850181.0
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "Is ACRONYM collaborating with @Nike on an Air Force 1? Video: younglord (Instagram) https://t.co/QLnQQByALw " STOCK: Nike DATE: 01/02/2017
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: Nike 1_DAY_RETURN: -0.002263296869106 2_DAY_RETURN: 0.0011316484345529 3_DAY_RETURN: 0.0032063372312333 7_DAY_RETURN: 0.0158430780837419
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: Nike LAST_PRICE: 53.02 PX_VOLUME: 8810937.0 VOLATILITY_10D: 8.269 VOLATILITY_30D: 15.483 LSTM_POLARITY: -1 TEXTBLOB_POLARITY: 0.0
Predicted 1_DAY_RETURN: -0.002263296869106 Predicted 2_DAY_RETURN: 0.0011316484345529 Predicted 7_DAY_RETURN: 0.0158430780837419
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "RT @neymarjr: Glad to see another champion on team @Gillette. Welcome, @xPekeLoL. #PursuitofPrecision https://t.co/q4RdjwvJyN " STOCK: Gillette DATE: 01/02/2017
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 0.5.
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends.
STOCK: Gillette 1_DAY_RETURN: 0.0001778536620069 2_DAY_RETURN: 0.0018378211740713 3_DAY_RETURN: 0.0011382634368442 7_DAY_RETURN: 0.0012686894556493
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: Gillette LAST_PRICE: 4216.95 PX_VOLUME: 3863.0 VOLATILITY_10D: 3.391 VOLATILITY_30D: 4.525 LSTM_POLARITY: 1 TEXTBLOB_POLARITY: 0.5
Predicted 1_DAY_RETURN: 0.0001778536620069 Predicted 2_DAY_RETURN: 0.0018378211740713 Predicted 7_DAY_RETURN: 0.0012686894556493
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "RT @EbyFordLincoln: Congrats Jeanne & Stan Atkinson of #Goshen on ur 2017 @Ford Explorer https://t.co/ysoyhb9mcs Thank you folks @EbyFordLi…" STOCK: nan DATE: nan
Sentiment: (Provide sentiment here) Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is nan and the TextBlob polarity score is nan.
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends.
STOCK: nan 1_DAY_RETURN: nan 2_DAY_RETURN: nan 3_DAY_RETURN: nan 7_DAY_RETURN: nan
The stock shows a neutral return trend over the specified periods.
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN.
STOCK: nan LAST_PRICE: nan PX_VOLUME: nan VOLATILITY_10D: nan VOLATILITY_30D: nan LSTM_POLARITY: nan TEXTBLOB_POLARITY: nan
Predicted 1_DAY_RETURN: nan Predicted 2_DAY_RETURN: nan Predicted 7_DAY_RETURN: nan
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "Ford" STOCK: 01/02/2017 DATE: 12.32
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 @Ford.
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: 0.0040584415584414 2_DAY_RETURN: 0.0137987012987012 3_DAY_RETURN: 0.0381493506493505 7_DAY_RETURN: 44396812.0
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: 0.0032467532467531 PX_VOLUME: 26.327 VOLATILITY_10D: 27.639 VOLATILITY_30D: 1.0 LSTM_POLARITY: 0.0 TEXTBLOB_POLARITY: @Ford
Predicted 1_DAY_RETURN: 0.0040584415584414 Predicted 2_DAY_RETURN: 0.0137987012987012 Predicted 7_DAY_RETURN: 44396812.0
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: 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 @fox5dc: CEO Jeff Bezos says Amazon backs suit opposing Trump order https://t.co/MAMJE9AG8v @amazon @JeffBezos… " 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: 01/02/2017 DATE: 832.35
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 @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: 01/02/2017 1_DAY_RETURN: -0.0023667928155223 2_DAY_RETURN: 0.0041088484411605 3_DAY_RETURN: 0.0050099116958009 7_DAY_RETURN: 3850181.0
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: -0.0106565747582146 PX_VOLUME: 14.201 VOLATILITY_10D: 16.989 VOLATILITY_30D: 1.0 LSTM_POLARITY: 0.0 TEXTBLOB_POLARITY: @amazon
Predicted 1_DAY_RETURN: -0.0023667928155223 Predicted 2_DAY_RETURN: 0.0041088484411605 Predicted 7_DAY_RETURN: 3850181.0
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: 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 @RhondaHanks1: Becoming You! Evolving 😂@orangeistheneww #OITNB @OITNBNews @netflix https://t.co/KrsylAUOUL" 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 0.5625 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: 0.5625 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: "RT @CBS: Hungry for🍩? Follow @SuperiorDonuts, tell us your fav kind of donut w/ #Tweet4Sweets & you could win a dozen donuts… " STOCK: CBS DATE: 01/02/2017
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: CBS 1_DAY_RETURN: -0.0017027863777089 2_DAY_RETURN: -0.0030959752321979 3_DAY_RETURN: 0.0021671826625387 7_DAY_RETURN: -0.024767801857585
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: CBS LAST_PRICE: 64.6 PX_VOLUME: 2123243.0 VOLATILITY_10D: 13.477 VOLATILITY_30D: 16.281 LSTM_POLARITY: 1 TEXTBLOB_POLARITY: 0.0
Predicted 1_DAY_RETURN: -0.0017027863777089 Predicted 2_DAY_RETURN: -0.0030959752321979 Predicted 7_DAY_RETURN: -0.024767801857585
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "@krazy_kriz3253 @verizon @sprint @ATT @TMobile more coverage than Sprint in the U.S" 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: "AT&T" STOCK: 01/02/2017 DATE: 42.06
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 @ATT.
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends.
STOCK: 01/02/2017 1_DAY_RETURN: -0.0057061340941512 2_DAY_RETURN: -0.0011887779362816 3_DAY_RETURN: -0.0159296243461721 7_DAY_RETURN: 32082528.0
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: 0.0023775558725628 PX_VOLUME: 14.7 VOLATILITY_10D: 15.146 VOLATILITY_30D: 1.0 LSTM_POLARITY: 0.5 TEXTBLOB_POLARITY: @ATT
Predicted 1_DAY_RETURN: -0.0057061340941512 Predicted 2_DAY_RETURN: -0.0011887779362816 Predicted 7_DAY_RETURN: 32082528.0
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "@TMobile can I port my 📱" 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: "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 0.0 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: 0.0 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: "@HYPEBEAST @Nike thought they already made those" STOCK: nan DATE: nan
Sentiment: (Provide sentiment here) Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is nan and the TextBlob polarity score is nan.
Compare the stock returns over 1, 2, 3, and 7 days after being mentioned in the tweet. Highlight any significant patterns or trends.
STOCK: nan 1_DAY_RETURN: nan 2_DAY_RETURN: nan 3_DAY_RETURN: nan 7_DAY_RETURN: nan
The stock shows a neutral return trend over the specified periods.
Based on the given stock information, predict the 1_DAY_RETURN, 2_DAY_RETURN, and 7_DAY_RETURN.
STOCK: nan LAST_PRICE: nan PX_VOLUME: nan VOLATILITY_10D: nan VOLATILITY_30D: nan LSTM_POLARITY: nan TEXTBLOB_POLARITY: nan
Predicted 1_DAY_RETURN: nan Predicted 2_DAY_RETURN: nan Predicted 7_DAY_RETURN: nan
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "Nike" STOCK: 01/02/2017 DATE: 53.02
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 @Nike.
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: 0.0011316484345529 2_DAY_RETURN: 0.0032063372312333 3_DAY_RETURN: 0.0158430780837419 7_DAY_RETURN: 8810937.0
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: -0.002263296869106 PX_VOLUME: 8.269 VOLATILITY_10D: 15.483 VOLATILITY_30D: -1.0 LSTM_POLARITY: 0.0 TEXTBLOB_POLARITY: @Nike
Predicted 1_DAY_RETURN: 0.0011316484345529 Predicted 2_DAY_RETURN: 0.0032063372312333 Predicted 7_DAY_RETURN: 8810937.0
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "@adidas @JHarden13 trying so hard to get those new hardens!!! Gonna take the dopest photos for you guys!!" 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: "adidas" STOCK: 01/02/2017 DATE: 147.54
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 -0.012665719696969724 and the TextBlob polarity score is @adidas.
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: 0.0196624644164295 2_DAY_RETURN: 0.0426392842618952 3_DAY_RETURN: 0.0431001762233971 7_DAY_RETURN: 813955.0
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: -0.007726718178121 PX_VOLUME: 21.006 VOLATILITY_10D: 19.114 VOLATILITY_30D: 1.0 LSTM_POLARITY: -0.012665719696969724 TEXTBLOB_POLARITY: @adidas
Predicted 1_DAY_RETURN: 0.0196624644164295 Predicted 2_DAY_RETURN: 0.0426392842618952 Predicted 7_DAY_RETURN: 813955.0
Analyze the sentiment expressed in the tweet. Is it positive, negative, or neutral? Explain the sentiment in relation to the stock mentioned.
TWEET: "RT @2ALAW: Everyday for the last 7 years straight...and I'm talking 365 days a year I have purchased my coffee at @Starbucks 😡… " 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: 01/02/2017 DATE: 53.9
Sentiment: (Provide sentiment here) Explanation: The tweet sentiment is related to the stock mentioned, and it's important to interpret the context. The LSTM polarity score is -0.1 and the TextBlob polarity score is @Starbucks.