in their sleek machines

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The end came much as we expected, with our hands around each others throats, scrambling madly for the last dried-out crumbs. Overhead the vultures circle thirstily, their sleek silver machines hanging suspended in the rarefied air.

we didn’t mean to burn the forest down

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More Verin Mathwin

at the jetty we can see the wind
blowing out to stir the sailboat’s cloth
nothing lives outside the stress-torn sand
we live on, shoulder to shoulder at the
end of a roiling eternity
we didn’t mean to set the forest on fire
the great deserts in the center of it all
stand mute proof to the foolishness of
apologies and apologia and all apologism

The Best Albums of 2019, #20-01

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#20: (Sandy) Alex G – House Of Sugar

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Irrepressible, off-the-wall, and more than a little absurd, indie musician (Sandy) Alex G has made a career out of two things since dropping his debut in 2014: being as prolific as Ty Segall and being even more willing to play whatever the hell has come into his head in the last five minutes. House of Sugar marks his first album not put together in his bedroom but it keeps the manic, playlist-on-shuffle feel of his previous music. There’s just MORE of it – more instruments, more voices, more ideas.

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The 100 Best Albums of 2019, #40-21

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#40: Billie Eilish – When We Fall Asleep, Where Do We Go?

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Teen pop phenoms are almost always obnoxious – Donny Osmond and Justin Beiber were both awful in their own special ways. 2019’s teen pop phenom, Billie Eilish, manages to avoid this through the virtue of being really ridiculously good. Someone online – I forget who – called her ASMR pop and there’s a lot to that, really. Her style is like she took the mic into her closet and whispered her darkest secrets into it; these Whisper confessions were then laced over solid arrangements that both embrace and subvert pop conventions. An insane debut for a 17 year old, and a harbinger of big things to come.

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The 100 Best Albums of 2019, #60-41

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#60: Hot Chip – A Bath Full Of Ecstasy

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Hot Chip have put out their fair share of mediocre songs, but they’ve somehow avoided putting out a bad album. A Bath Full Of Ecstasy follows in that tradition; it presents a series of solid dance floor grooves that have the usual dark concerns laced under it – abandonment, the absurdity of existence, uncertainty of faith. Like the Pet Shop Boys and Depeche Mode before them, Hot Chip have always known that there is more to the club than escapist bliss.

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The 100 Best Albums of 2019, #80-61

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#80: King Gizzard and the Lizard Wizard – Fishing For Fishies

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Album #1 from the absurdly prolific Aussie psych band takes a dive back into the feel-good post-psych of the Seventies and reinvents the band’s sound for the umpteenth time in order to be a feel-good prog-funk band. What if Chilliwack was actually good? Gizzard answers the question no one was asking.

Also, uh, that album cover.

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The 100 Best Albums of 2019, #100-81

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As far as years to end decades on, you could do worse than 2019. You could do better, of course; both 1969 and 1989 were world-shakers when it came to music (among everything else). But it’s not like 2019 was 2009, when the best album came out in January and everything else was just sort of okay after that. It wasn’t 1999 either, when we were mired in nu-metal and rap-rock, hip hop was still stuck in it’s Gucci-vacation mode, and jazz was still something for old people to tap their toes to while they were waiting on the final heart attack. That year gave us Woodstock ’99, and the less said of that the better.

Rock ‘n’ roll didn’t fare very well throughout the decade, depending on your perspective. A lot of it’s best moments were pretty underground; mainstream rock is a horrorshow that can be best encapsulated in that Billboard chart of the best rock songs of the decade that has numbers one through three occupied by Imagine Dragons. Hip hop, though, has progressed rapidly and weirdly through a strong experimental phase, the haters be damned. The comeback of jazz is in many ways the story of music in the teens, or the tens, or whatever we’re calling this past decade. There will be a number of entries in these three categories and more on this list, of course, but it’s good to take these final entries and use them to take stock of where we’ve been. For many artists, taking the decade challenge is extremely instructive, especially for the one sitting at the #1 spot this year. This is true of many of the artists in the top 20, several of whom were forging names for themselves in the underground in 2009, and others who were at a career crossroads back then.

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#ELXN43 on #CdnPoli, Day 4

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Another day, another set of data points to add to the list.

day4

Bad day for the CPC, their opposers managed to overwhelm attempts at boosters to get some positive messaging flowing on Twitter. Greens and NDP had good days, although it really does seem like the story of the NDP on Twitter is one of relentless optimism, unlike the numbers coming out of polling organizations where at best they will bleed out seats and at worst they may lose Official Party status. Trudeau and Scheer both get negative sentiment for the day which is probably pretty normal considering they’re the top two candidates in the election and are thus targets for each others partisan keyboard warriors. The May negative turn is interesting and probably has a lot to do with ongoing scandals involving Green candidates proposing to potentially re-open the abortion debate and supporting Quebec separatism. Sentiment for the Green Party as a whole is up though, so it may be that she is taking the brunt of the controversy on herself (not that she doesn’t have her own problems *cough* wifi *cough*). Overall sentiment on #CdnPoli was down; I’m expecting that measure to bounce up and down over the 0-mark for the entire election, based on previous experience with sentiment analysis both on Twitter and through previous work on sentiment in fiction.

Campaign to date for parties:

day1to4

And campaign to date for leaders:

day1to4leaders

ELXN43 and #CdnPoli: Exploring Sentiment on Political Twitter

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File it under “Fun Things To Do During An Election.” We’re going to track the sentiment scores of tweets on the ‘official’ Canadian politics hashtag on Twitter, #CdnPoli, first and foremost just to gather the data but also to see if we can learn anything about the political process on Twitter. There are any number of research questions that one can use to approach this data set but for right now we’re just going to use what I refer to only half-jokingly as the Armstrong Method: start with the data and then explore it to see if any patterns leap out at you, before you start making assumptions about it. It’s a much more inductive process and one that this type of data set seems to really need.

So how does this work? First we scrape #CdnPoli, of course. I have a standard script I like to use to do that using the twitteR package in R; I’m aware that RTweet is newer, better (and, more importantly, maintained) and I use that as well, but for some reason I can’t get RTweet scripts to schedule properly using Windows Task Scheduler so for right now the workflow is: scrape tweets initially using twitteR, and then run them through an iterated loop that re-grabs each tweet in RTweet. This is not ideal obviously and takes slightly longer than if I could just get RTweet to schedule properly, but it does have the added bonus of eliminating duplicate tweets without having to take an extra step, so maybe it washes out.

Tweets get scraped on an hourly basis and then stitched together at the end of the day into one csv file. The Hu & Liu sentiment lexicon is used to provide scores for sentiment in each tweet. The Hu & Liu lexicon is a dictionary of around 6800 English words that are coded to denote positive or negative sentiment. If a tweet contains a word in the lexicon, the sentiment score of the tweet is adjusted up or down as necessary. This is a fairly basic application of sentiment analysis but it does allow for some numerical representation of the sentiment of a tweet.

Next, regular expressions are used to identify tweets referring to each of the five ‘parties of interest’ in the 2019 Canadian federal election and to each of the leaders of those parties. Separating each of these out allows us to determine a mean score for tweets mentioning that party or party leader. We can then track that sentiment day-by-day (I mean, we could do it hour by hour but come on, I have grad work I’m supposed to be doing).

Just as a visual example of what this is, here’s a bar chart for Day 1 (September 11th, 2019):

day1

So, on the first day of the campaign, tweets using the #CndPoli hashtag were quite negative with regard to both the Liberal Party and Prime Minister Justin Trudeau; they were also negative (although not quite as negative) with regard to the Conservative Party and CPC leader Andrew Scheer. Positive opinions were characteristic of tweets regarding the New Democratic Party, the Green Party, the People’s Party, and their respective leaders. Overall, the sentiment on #CdnPoli for Day 1 was slightly negative.

One of the possibilities for the sentiment scores we’ll see over the election is that they may be a measure for how energetic the respective campaign is. About 90% of Canada is online, and 61% of Canadians use social media on a daily basis. 77% of Canadians use Facebook, whereas 26% use Twitter. Despite this, Twitter makes for a more interesting data source for two reasons: first, Twitter posts are publicly available and Facebook posts are jealously guarded by Facebook; second, natural limitations on Twitter posting make for a more level playing field with which to analyze posts. Posts cannot be longer than 280 characters, which eliminates the scaling problem that typically occurs when you have texts of wildly differing lengths to analyze. Everyone’s posts are around the same length, so we can compare one tweet to the next in terms of sentiment scores derived from them. The use of hashtags as organizing containers on Twitter makes it easy to track specific phenomena of interest, which means that data gathering can be accomplished with relative ease.

The rationale for sentiment scores being a function of campaign energy lies in the relatively low use of Twitter by Canadians. If only 26% of Canadians are using Twitter, then the ones who are both using the platform and are specifically engaging on an explicitly political hashtag are likely to be more engaged politically than the average person. They are more likely to be partisan supporters of a specific party (or at least a range of parties within a certain ideological range). Thus, we can think of it as persons performing one of two activities: boosting support for their chosen party by expressing positive sentiment, or attacking an opposing party by expressing negative sentiment. If supporters can manage to boost their chosen party more than opposers can manage to bring down their opposing party, then sentiment will rise day-over-day. This represents an energized campaign. If the opposite occurs, and supporters can not express enough positive sentiment to overcome the negative sentiment of their opposite numbers, then sentiment will fall day-over-day. This represents a de-energized campaign.

Granted, there are also users of #CdnPoli who are not hard supporters of one party or another; these can be thought of as ‘undecided’ voters reacting to acute or aggregate events during the campaign. These will also effect sentiment scores on a daily basis; if a particular event causes negative sentiment outside of social media toward a party, then their supporters will have a harder time boosting the party through positive sentiment tweets.

At least, that’s a theory. A proper academic treatment of the subject after the fact will require some grounding in the literature obviously but that’s at least something to hang our hats on with regard to tracking it during the campaign. In the end, we’ll see what the data says.

As an example of tracking change day-over-day, here’s each party by day for the first three days of the campaign:

day1to3

and the same, but for party leaders:

day1to3leaders

Note the upswing for May and Singh and their respective parties after day 2; the first debate occurred on that day and we can see a sharp effect for them and a slight upswing for Scheer. These three leaders participated in the debate, whereas Trudeau chose not to take part (Bernier was not invited). Despite this, we see a downward trend on that day for Scheer’s Conservative Party and a sharp upward trend for the ruling Liberal Party. Part of this likely has to do with perceptions gained from the debate; I’ll post a bar chart of a separate analysis of just the #FirstDebate hashtag later, but the general idea is that Jagmeet Singh won the sentiment battle there, while Scheer underperformed and in fact garnered less positive sentiment than Prime Minister Trudeau, who wasn’t even there.

At any rate, that’s the idea. Let’s see what the data tells us from here on out.