Discover and read the best of Twitter Threads about #Sentiment

@Wolf_of_Ballaro

1/ Negli ultimi giorni c’è molto timore dietro i token wrapped di Bitcoin. Ho già sollevato qualche qualche giorno fa la questione di wBTC, ma facciamo un piccolo recap perché ci sono delle cose che DEVI sapere!

🧵

2/ C’è una forte incertezza dietro al token #renBTC per via del suo legame con #alamedaresearch. Ma, a differenza di #wBTC possiamo vedere che il suo valore è ancora peggato, sostanzialmente perché c’è ancora il collaterale a sostenerne il prezzo

coingecko.com/it/monete/renb…

3/ La bancarotta di #alameda ha prosciugato le casse del team che sta dietro il progetto. Per questo chiuderà l’attuale versione di #ren in favore di una sorta di #renDAO. Qui @renprotocol ci spiega tutti i dettagli:

https://twitter.com/renprotocol/status/1594305211115663360?s=20&t=gah93wkMy4D2KgbDLHiaUQ

Read 14 tweets

Alpha Drops

@MyAlphaDrops

Sentiment is live on #Arbitrum ⚡️

@sentimentxyz is a liquidity protocol enabling on-chain permissionless undercollateralized borrowing. The protocol reached its $500,000 lending cap within 30 seconds of going live! 🎉

🪂 Like & RT

🧵 Thread 👇
1/

@sentimentxyz is introducing a unique approach for lenders and borrowers to maximize the utility of their capital by enabling borrowing of up to 500% LTV through unique delegated ownership accounts.

2/

Sentiment supports a wide range of lend/borrow token types and has integrated some of the most popular #DeFi protocols, and more protocols are going to be integrated soon.

3/

Read 6 tweets

Lance Roberts

@LanceRoberts

The #BullBearReport is out.
The #market started with a strong #rally, then #stumbled into week's end. With #oversold conditions still intact and CPI next week, the #bulls have a shot at a rally if #inflation shows some signs of cooling.
realinvestmentadvice.com/market-rally-s…

Moving forward, YoY comparisons become more challenging at 0.9%, 0.7%, 0.6%, 0.6%, 0.8%, and 1.2% through March 2023. In August, #inflation will drop to 7.9% from 9% in June. Assuming an avg. 0.2% MoM increase, CPI hits the Fed’s 2% target in June 2023.
realinvestmentadvice.com/market-rally-s…

Despite Friday's rout, the #market did finish the week in positive territory. The deeply oversold indicators turned up and are close to triggering “buy signals.” The market is trading 2-standard deviations below the 50- WEEK moving average.
realinvestmentadvice.com/market-rally-s…

Read 6 tweets

Alan Kooper

@alan_kooper

#RiskOn / #RiskOff Zde mé slíbené vysvětlení. Nevím co znamená RiskOn/Off pro jiné systémy nebo interpretace. Možná jsem mohl použít jiné názvy. Já to mám ale vžité už léta a prostě jsem to použil. Pokud se to používá i někde jinde, půjde na 100% o něco jiného než o co jde mě 1/n

Sledují mě tu stále nové a nové lidi a chápu, že to může působit možná až jako nějaké „čárymáry“. Že si tady tak zvesela měřím geo-politcké dění na SP500 a interpretuji to v grafech. Je to ale prostě úplně obráceně. Mě osobně přijde neuvěřitelné spíše to, že spousta 2/n

lidí/obchodníků něco střílí od boku jen na bázi toho, že si to myslí, že mají pocit. To mě k moodixu vlastně dovedlo. To nekonečné množství názorů, ego her. Vlastně hlavně ego her. Boj o to kdo trefí tohle dno, tuhle otočku, tenhle krach, tuhle rally a bude plácán po zádech. 3/n

Read 25 tweets

Stephen.⦻

@the_economystic

1/ What up! 😆✌️ BULLISH #EGLD Market Update T.A. ⚡ @ElrondNetwork - Couple days ago U saw my market update with bearish perspectives, but U know my perma-bull never left! 👉 Balancing perspectives is key! 🔑 Expect downside yet maintain exposure to capture the upside! 🧠🔥🧵

2/ Before we get into it, some key elements to the plan for success: 📝💡

→ Capture the average
→ Make few trades
→ Maintain exposure

Your brain is a muscle, therefore I remind U the importance of muscle memory & that it applies to trading! Train regularly & consistently.

3/ When training 🏋️ it's vital to develop muscle memory with proper technique AND recovery! We must give our muscles the proper time ⌛ and nutrition 🥩🥗🥛🥔 to recover. 😴

In trading 📈 this equates to sitting on sidelines while volatility seeks to suck U in.

Read 25 tweets

Material Indicators

@MI_Algos

1/ Bear markets are notorious for brutal fakeouts, which is why it's critical to identify targets and invalidations.

Here are key levels to watch in both directions.📉📈

#BTC analysis 🧵
#NFA

More from Material Indicators here... mi1.pw/mitwmay

2/In bear markets everyone is looking for a relief rally to exit or add short. Those are good strategies if you can tell the difference between a fakeout and a breakout. #FOMO + failure to identify invalidation levels are why so many people get rekt. #BullTrap #ShortSqueeze

3/ Identifying targets is easy. Determining breakout or fakeout is more challenging, and while there are no sure things, there are some things we can look at to help mitigate risk of getting trapped. To get some perspective, let's start with an ultra wide macro view of #BTC

Read 17 tweets

Tiho Brkan

@TihoBrkan

1/ The recent survey of global fund managers by Merrill Lynch is quite interesting from a contrarian's perspective.

Over 300 money managers with circa trillion in assets under management very polled on the various portfolio, finance & economy questions.

#sentiment update 👇

2/ Cash balance happens to be the highest since September 11 crash in 2001 (@ 6.1%). 😲

To say they are quite worried would be an understatement.

Yes, it is a small sample size (the law of small numbers leads to errors) but when others are fearful, we should be greedy.

3/ Do you remember that classic line from Back To The Future:

"Marty McFly, are you chicken?" 😂

It looks like global managers have chickened out from taking risks, as their exposure to risk sinks to the lowest since the Global Financial Crisis.

The sentiment is in the gutter.

Read 20 tweets

Lena Hipp

@llhipp

Can computational methods assess the #sentiment of complex texts? In an article with my fabulous colleagues from the @WZB_Berlin, @unipotsdam @LMU_Muenchen @JungeAkademie, we answer this question by applying #dictionary and #scaling methods on a sample of #literature reviews🧵

The article is also a practical #guide to help researchers select an appropriate #method and degree of preprocessing for their own data. Our #corpus consisted of 6.041 summaries of reviews of contemporary German #literature acquired from @perlentaucher00 (2/9)

The linguistic #complexity of #literature reviews differ from other texts with regard to their #language-- ambiguity, irony, metaphors, etc.-- are comparatively difficult to capture with #computational approaches. So how did our different methods fare? (3/9)

Read 9 tweets

Websitecategorization

@webcategories

kategorisierungen.substack.com/p/stimmungsana…
Artikel über #Sentiment Analyse and #Meinungsforschung.

another post, on #predictiveanalytics kategorisierungen.substack.com/p/pradiktive-a…

#website #categorization article on #substack
kategorisierungen.substack.com/p/website-kate…

Read 6 tweets

Serpentarius Sagittarius ♏️⛎♐️

@Cryptographiste

Hence the absolute need to get out of this voluntary submission system established for at least 300 years.
If you have any suggestions, put them below.
Be creative & let's brainstorm together to perhaps allow us to break these chains of oppression.
#Psychologicalreactance
⛎

https://twitter.com/betterworld_22/status/1512144875956981764

#Naturopathy
#Herbalism
#Phytotherapy
#Nutraceuticals

#Miraculin
@CleoKapp

#Naturopathy
#Herbalism
#Phytotherapy
#Nutraceuticals

#AilanthusAltissima (Mill.) Swingle, 1916
False Japanese varnish, Ailante, Ailanth
« Dwarf Ailanthus is also sometimes nicknamed "the tree of hell" because of its invasive nature and the difficulty of eradicating it. »

Read 5 tweets

Liqian Ren

@liqian_ren

1. A thread on three #PBOC 2021 Q2 Macro sentiment surveys from Bankers (Sample 3200), Industrial Companies (5000) and Urban Bank Customers (20,000). Surveys data are considered less informative and need to be heavily discounted, but could be complimentary to other data source.

2. Though data came from two diff. surveys, bankers and entrepreneurs have similar view:

Entrepreneur(Bankers) view on Macroeconomy 23.9%(25.5%) as “Cold”，72.6%(71.8%) “Normal”，3.5(2.7%)% “Hot”. Normal got 50 and HOT got 100. Indexes recovered to pre-Pandemic, Q3 lower than Q2

3. Product Selling Price (Raw Material Purchasing) Sentiment, 24.4%(47.1%) expect “Increase”，67.6% (49.6%) "Same"，8.0%(3.2%) "Decrease”. Expect China PPI/CPI finally converge somewhat as companies raise prices on consumers. Some Q3 pull back from Q2.

Read 14 tweets

Rick Rieder

@RickRieder

In August we saw #inflation growth moderate further, for the second consecutive month, at least relative to the impressive rate of growth in #prices witnessed around mid-year.

Core #CPI (excluding volatile food and energy components) came in at 0.10% month-over-month and 3.98% year-over-year, which was considerably less than the consensus forecast and was driven higher by #shelter components.

Meanwhile, headline #CPI data printed at a solid 0.27% month-over-month and came in at 5.20% year-over-year.

Read 11 tweets

Grant Hadlich

@GrantHadlich

How negative was my Twitter feed in the last few hours? In the replies are a few models that analyze the sentiment of my home timeline feed on Twitter for the last 24 hours using the Twitter API.
GitHub: github.com/ghadlich/Daily…
#NLP #Python

I analyzed the sentiment on the last 253 tweets from my home feed using a pretrained #BERT model from #huggingface. A majority (70.0%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot

I analyzed the sentiment on the last 253 tweets from my home feed using a pretrained #VADER model from #NLTK. A majority (56.1%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot

Read 7 tweets

Grant Hadlich

@GrantHadlich

I analyzed the sentiment on the last 272 tweets from my home feed using a pretrained #BERT model from #huggingface. A majority (69.9%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot

I analyzed the sentiment on the last 272 tweets from my home feed using a pretrained #VADER model from #NLTK. A majority (57.0%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot

Read 7 tweets

Grant Hadlich

@GrantHadlich

I analyzed the sentiment on the last 378 tweets from my home feed using a pretrained #BERT model from #huggingface. A majority (68.0%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot

I analyzed the sentiment on the last 378 tweets from my home feed using a pretrained #VADER model from #NLTK. A majority (60.3%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot

Read 7 tweets

Grant Hadlich

@GrantHadlich

I analyzed the sentiment on the last 476 tweets from my home feed using a pretrained #BERT model from #huggingface. A majority (70.0%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot

I analyzed the sentiment on the last 476 tweets from my home feed using a pretrained #VADER model from #NLTK. A majority (60.5%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot

Read 7 tweets

Grant Hadlich

@GrantHadlich

I analyzed the sentiment on the last 528 tweets from my home feed using a pretrained #BERT model from #huggingface. A majority (68.0%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot

I analyzed the sentiment on the last 528 tweets from my home feed using a pretrained #VADER model from #NLTK. A majority (58.9%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot

Read 7 tweets

Grant Hadlich

@GrantHadlich

I analyzed the sentiment on the last 569 tweets from my home feed using a pretrained #BERT model from #huggingface. A majority (65.4%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot

I analyzed the sentiment on the last 569 tweets from my home feed using a pretrained #VADER model from #NLTK. A majority (53.6%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot

Read 7 tweets

Grant Hadlich

@GrantHadlich

I analyzed the sentiment on the last 239 tweets from my home feed using a pretrained #BERT model from #huggingface. A majority (61.1%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot

I analyzed the sentiment on the last 239 tweets from my home feed using a pretrained #VADER model from #NLTK. A majority (61.5%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot

Read 7 tweets

Grant Hadlich

@GrantHadlich

I analyzed the sentiment on the last 288 tweets from my home feed using a pretrained #BERT model from #huggingface. A majority (65.6%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot

I analyzed the sentiment on the last 288 tweets from my home feed using a pretrained #VADER model from #NLTK. A majority (62.8%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot

Read 7 tweets

Grant Hadlich

@GrantHadlich

I analyzed the sentiment on the last 412 tweets from my home feed using a pretrained #BERT model from #huggingface. A majority (67.5%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot

I analyzed the sentiment on the last 412 tweets from my home feed using a pretrained #VADER model from #NLTK. A majority (59.0%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot

Read 7 tweets

Discover and read the best of Twitter Threads about #Sentiment

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Discover and read the best of Twitter Threads about #Sentiment

Most recents (24)

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