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What is the general outlook on RTX 4060 GPU, the whole generation of the chip and the company?
Here you can find sql queries for getting values from data
WITH sentiment_sums AS (SELECT SUM(CASE WHEN sentiment = 0 THEN likes ELSE 0 END) AS neg_likes_total,
SUM(CASE WHEN sentiment = 1 THEN likes ELSE 0 END) AS neu_likes_total,
SUM(CASE WHEN sentiment = 2 THEN likes ELSE 0 END) AS pos_likes_total,
SUM(CASE WHEN sentiment = 0 THEN views ELSE 0 END) AS neg_views_total,
SUM(CASE WHEN sentiment = 1 THEN views ELSE 0 END) AS neu_views_total,
SUM(CASE WHEN sentiment = 2 THEN views ELSE 0 END) AS pos_views_total,
SUM(CASE WHEN sentiment = 0 THEN comments + reposts ELSE 0 END) AS neg_shares_total,
SUM(CASE WHEN sentiment = 1 THEN comments + reposts ELSE 0 END) AS neu_shares_total,
SUM(CASE WHEN sentiment = 2 THEN comments + reposts ELSE 0 END) AS pos_shares_total,
COUNT(*) AS total_amount_of_posts
FROM processed_nvidia), -- Name of the table should be changed hor two observations
weights AS (SELECT CAST(neg_likes_total AS DOUBLE PRECISION) / neg_views_total AS neg_likes_per_view,
CAST(neu_likes_total AS DOUBLE PRECISION) / neu_views_total AS neu_likes_per_view,
CAST(pos_likes_total AS DOUBLE PRECISION) / pos_views_total AS pos_likes_per_view,
CAST(neg_likes_total AS DOUBLE PRECISION) / neg_shares_total AS neg_likes_per_share,
CAST(neu_likes_total AS DOUBLE PRECISION) / neu_shares_total AS neu_likes_per_share,
CAST(pos_likes_total AS DOUBLE PRECISION) / pos_shares_total AS pos_likes_per_share
FROM sentiment_sums),
totals AS (SELECT weights.neg_likes_per_view * sentiment_sums.neg_views_total + 1 * sentiment_sums.neg_likes_total +
sentiment_sums.neg_shares_total * weights.neg_likes_per_share AS neg_total,
weights.neu_likes_per_view * sentiment_sums.neu_views_total + 1 * sentiment_sums.neu_likes_total +
sentiment_sums.neu_shares_total * weights.neu_likes_per_share AS neu_total,
weights.pos_likes_per_view * sentiment_sums.pos_views_total + 1 * sentiment_sums.pos_likes_total +
sentiment_sums.pos_shares_total * weights.pos_likes_per_share AS pos_total
FROM sentiment_sums,
weights)
SELECT totals.neg_total / sentiment_sums.total_amount_of_posts AS neg_avg,
totals.neu_total / sentiment_sums.total_amount_of_posts AS neu_avg,
totals.pos_total / sentiment_sums.total_amount_of_posts AS pos_avg,
totals.neg_total,
totals.neu_total,
totals.pos_total
FROM sentiment_sums,
weights,
totals;
Measurement
neg_total
neu_total
pos_total
neg_avg
neu_avg
pos_avg
Competitors
82140
127239
229482
33.78856437679967
52.340189222542165
94.39819004524887
Nvidia
142338
1124049
1702794
36.86557886557887
291.1289821289821
441.024087024087
Now we can go to another question:
What countries were the most involved in the discussion?
SELECT SUM(CAST(content ILIKE '%naira%' OR content ILIKE '%ngn%' OR content ILIKE '%nigeria%' AS INTEGER)) AS nigeria_amount,
SUM(CAST(content ILIKE '%kes%' OR content ILIKE '%kenya%' AS INTEGER)) AS kenya_amount,
SUM(CAST(content ILIKE '%euro%' OR content ILIKE '%eur%' AS INTEGER)) AS europe_amount,
SUM(CAST(content ILIKE '%dollar%' OR content ILIKE '%usd%' OR content ILIKE '%us dollar%' AS INTEGER)) AS us_amount,
SUM(CAST(content ILIKE '%gbp%' OR content ILIKE '%pounds%' AS INTEGER)) AS gbp_amount,
SUM(CAST(content ILIKE '%chf%' OR content ILIKE '%franc%' AS INTEGER)) AS swiss_amount,
SUM(CAST(content ILIKE '%ghana%' OR content ILIKE '%ghc%' AS INTEGER)) AS ghana_amount,
SUM(CAST(content ILIKE '%rupee%' OR content ILIKE '%inr%' AS INTEGER)) AS india_amount,
SUM(CAST(content ILIKE '%cny%' OR content ILIKE '%yuan%' AS INTEGER)) AS china_amount,
SUM(CAST(content ILIKE '%jpy%' OR content ILIKE '%yen%' AS INTEGER)) AS japan_amount
FROM processed_competitors; -- This should be changed to table name on different observations
Measurement
nigeria
kenya
europe
usa
gdp
swiss
ghana
india
china
japan
Competitors
24
58
34
9
15
4
40
2
0
0
Nvidia
461
78
23
23
15
8
7
2
2
1
Next question is:
Keywords Associated with the RTX 4000 series graphics card. (Keywords)
SELECT SUM(CAST(content ILIKE '%ample%' AS INTEGER)) AS goodword_ample,
SUM(CAST(content ILIKE '%high%' AS INTEGER)) AS goodword_high,
SUM(CAST(content ILIKE '%cool%' AS INTEGER)) AS goodword_cool,
SUM(CAST(content ILIKE '%top-notch%' OR content ILIKE '%top notch%' AS INTEGER)) AS goodword_top,
SUM(CAST(content ILIKE '%seamless%' AS INTEGER)) AS goodword_seamless,
SUM(CAST(content ILIKE '%amazing%' OR content ILIKE '%amaze%' AS INTEGER)) AS goodword_amaze,
SUM(CAST(content ILIKE '%beautiful%' OR content ILIKE '%beauty%' AS INTEGER)) AS goodword_beauty,
SUM(CAST(content ILIKE '%plentiful%' OR content ILIKE '%plenty%' AS INTEGER)) AS goodword_plenty,
SUM(CAST(content ILIKE '%efficient%' AS INTEGER)) AS goodword_efficient,
SUM(CAST(content ILIKE '%excellent%' OR content ILIKE '%excellency%' OR content ILIKE
'%excels%' AS INTEGER)) AS goodword_excellent,
SUM(CAST(content ILIKE '%great%' OR content ILIKE '%greatness%' OR content ILIKE '%greatest%' AS
INTEGER)) AS goodword_great,
SUM(CAST(content ILIKE '%innovative%' AS INTEGER)) AS goodword_innovative,
SUM(CAST(content ILIKE '%impressive%' OR content ILIKE '%impress%' AS INTEGER)) AS goodword_impress,
SUM(CAST(content ILIKE '%solid%' AS INTEGER)) AS goodword_solid,
SUM(CAST(content ILIKE '%flawless%' AS INTEGER)) AS goodword_flawless,
SUM(CAST(content ILIKE '%smooth%' AS INTEGER)) AS goodword_smooth,
SUM(CAST(content ILIKE '%fantastic%' AS INTEGER)) AS goodword_fantastic,
SUM(CAST(content ILIKE '%outstanding%' AS INTEGER)) AS goodword_outstanding,
SUM(CAST(content ILIKE '%good%' AS INTEGER)) AS goodword_good
FROM processed_competitors; -- This should be changed to table name on different observations
Keyword
amount_for_competitors
amount_for_nvidia
goodword_ample
4
7
goodword_high
175
255
goodword_cool
81
144
goodword_top
0
0
goodword_seamless
3
7
goodword_amaze
23
33
goodword_beauty
18
31
goodword_plenty
5
3
goodword_efficient
17
45
goodword_excellent
21
8
goodword_great
54
47
goodword_innovative
3
4
goodword_impress
25
31
goodword_solid
11
21
goodword_flawless
0
0
goodword_smooth
8
24
goodword_fantastic
15
7
goodword_outstanding
4
3
goodword_good
61
69
Table for badwords
SELECT SUM(CAST(content ILIKE '%insufficient%' OR content ILIKE '%insufficiency%' AS INTEGER)) AS badword_insufficient
, SUM(CAST(content ILIKE '%low%' OR content ILIKE '%lower%' OR content ILIKE '%lowest%' AS INTEGER)) AS badword_low
, SUM(CAST(content ILIKE '%overheating%' OR content ILIKE '%overheated%' AS INTEGER)) AS badword_overheating
, SUM(CAST(content ILIKE '%disappointing%' OR content ILIKE '%dissapointed%' AS INTEGER)) AS badword_disappointing
, SUM(CAST(content ILIKE '%limited%' OR content ILIKE '%limiting%' AS INTEGER)) AS badword_limited
, SUM(CAST(content ILIKE '%poor%' OR content ILIKE '%poorer%' OR content ILIKE '%poorest%' AS INTEGER)) AS badword_poor
, SUM(CAST(content ILIKE '%subpar%' AS INTEGER)) AS badword_subpar
, SUM(CAST(content ILIKE '%lacking%' OR content ILIKE '%lacked%' AS INTEGER)) AS badword_lacking
, SUM(CAST(content ILIKE '%power-hungry%' OR content ILIKE '%power hungry%' AS INTEGER)) AS badword_power_hungry
, SUM(CAST(content ILIKE '%terrible%' AS INTEGER)) AS badword_terrible
, SUM(CAST(content ILIKE '%underwhelming%' OR content ILIKE '%underwhelmed%' AS INTEGER)) AS badword_underwhelming
, SUM(CAST(content ILIKE '%struggling%' OR content ILIKE '%struggled%' AS INTEGER)) AS badword_struggling
, SUM(CAST(content ILIKE '%suboptimal%' OR content ILIKE '%sub optimal%' OR content ILIKE
'%sub-optimal%' AS INTEGER)) AS badword_suboptimal
, SUM(CAST(content ILIKE '%overhyped%' OR content ILIKE '%over hyped%' OR content ILIKE
'%over-hyped%' AS INTEGER)) AS badword_overhyped
, SUM(CAST(content ILIKE '%lacking%' OR content ILIKE '%lacked%' AS INTEGER)) AS badword_lacking
, SUM(CAST(content ILIKE '%bad%' AS INTEGER)) AS badword_bad
FROM processed_competitors; -- This should be changed to table name on different observations